11th Mediterranean Conference on Medical and Biological Engineering and Computing 2007: MEDICON 2007, 26-30 June 2007, Ljubljana, Slovenia (IFMBE Proceedings)

IFMBE Proceedings Series Editors: R. Magjarevic and J. H. Nagel

Volume 16/1

The International Federation for Medical and Biological Engineering, IFMBE, is a federation of national and transnational organizations representing internationally the interests of medical and biological engineering and sciences. The IFMBE is a non-profit organization fostering the creation, dissemination and application of medical and biological engineering knowledge and the management of technology for improved health and quality of life. Its activities include participation in the formulation of public policy and the dissemination of information through publications and forums. Within the field of medical, clinical, and biological engineering, IFMBE’s aims are to encourage research and the application of knowledge, and to disseminate information and promote collaboration. The objectives of the IFMBE are scientific, technological, literary, and educational. The IFMBE is a WHO accredited NGO covering the full range of biomedical and clinical engineering, healthcare, healthcare technology and management. It is representing through its 58 member societies some 120.000 professionals involved in the various issues of improved health and health care delivery. IFMBE Officers President: Makoto Kikuchi, Vice-President: Herbert Voigt, Past-President: Joachim H. Nagel Treasurer: Shankar M. Krishnan, Secretary-General: Ratko Magjarevic http://www.ifmbe.org

Previous Editions: IFMBE Proceedings MEDICON 2007 “11th Mediterranean Conference on Medical and Biological Engineering and Computing 2007”, Vol. 16, 2007, Ljubljana, Slovenia, CD IFMBE Proceedings BIOMED 2006 “Kuala Lumpur International Conference on Biomedical Engineering”, Vol. 15, 2004, Kuala Lumpur, Malaysia, CD IFMBE Proceedings WC 2006 “World Congress on Medical Physics and Biomedical Engineering”, Vol. 14, 2006, Seoul, Korea, DVD IFMBE Proceedings BSN 2007 “4th International Workshop on Wearable and Implantable Body Sensor Networks”, Vol. 13, 2006, Aachen, Germany IFMBE Proceedings ICBMEC 2005 “The 12th International Conference on Biomedical Engineering”, Vol. 12, 2005, Singapore, CD IFMBE Proceedings EMBEC’05 “3rd European Medical & Biological Engineering Conference, IFMBE European Conference on Biomedical Engineering”, Vol. 11, 2005, Prague, Czech Republic, CD IFMBE Proceedings ICCE 2005 “The 7th International Conference on Cellular Engineering”, Vol. 10, 2005, Seoul, Korea, CD IFMBE Proceedings NBC 2005 “13th Nordic Baltic Conference on Biomedical Engineering and Medical Physics”, Vol. 9, 2005, Umeå, Sweden IFMBE Proceedings APCMBE 2005 “6th Asian-Pacific Conference on Medical and Biological Engineering”, Vol. 8, 2005, Tsukuba, Japan, CD IFMBE Proceedings BIOMED 2004 “Kuala Lumpur International Conference on Biomedical Engineering”, Vol. 7, 2004, Kuala Lumpur, Malaysia IFMBE Proceedings MEDICON and HEALTH TELEMATICS 2004 “X Mediterranean Conference on Medical and Biological Engineering”, Vol. 6, 2004, Ischia, Italy, CD IFMBE Proceedings 3rd Latin – American Congress on Biomedical Engineering “III CLAEB 2004”, Vol. 5, 2004, Joao Pessoa, Brazil, CD IFMBE Proceedings WC2003 “World Congress on Medical Physics and Biomedical Engineering”, Vol. 4, 2003, Sydney, Australia, CD IFMBE Proceedings EMBEC'02 “2nd European Medical and Biological Engineering Conference”, Vol. 3, Parts 1 & 2, 2002, H. Hutten and P. Kroesl (Eds.), Vienna, Austria IFMBE Proceedings 12NBC “12th Nordic Baltic Conference on Biomedical Engineering and Medical Physics”, Vol. 2, 2002, Stefan Sigurdsson (Ed.) Reykjavik, Iceland IFMBE Proceedings MEDICON 2001 – “IX Mediterranean Conference on Medical Engineering and Computing”, Vol. 1, Parts 1 & 2, 2001, R. Magjarevic, S. Tonkovic, V. Bilas, I. Lackovic (Eds.), Pula, Croatia

IFMBE Proceedings Vol. 16/1 T. Jarm, P. Kramar, A. Županič (Eds.)

11th Mediterranean Conference on Medical and Biological Engineering and Computing 2007 MEDICON 2007, 26 – 30 June 2007 Ljubljana, Slovenia

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Editors Tomaž Jarm University of Ljubljana Faculty of Electrical Engineering Trzaska 25 1000 Ljubljana, Slovenia E-Mail: [email protected]

Anže Županič University of Ljubljana Faculty of Electrical Engineering Trzaska 25 1000 Ljubljana, Slovenia E-Mail: [email protected]

Peter Kramar University of Ljubljana Faculty of Electrical Engineering Trzaska 25 1000 Ljubljana, Slovenia E-Mail: [email protected]

Library of Congress Control Number: 2007928834 ISSN: 1680-0737 ISBN: 978-3-540-73043-9 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable for prosecution under the German Copyright Law. The IFMBE Proceedings is an Offical Publication of the International Federation for Medical and Biological Engineering (IFMBE) Springer is a part of Springer Science+Business Media springer.com © International Federation for Medical and Biological Engineering 2007 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Data supplied by the authors Production: Le-Tex Jelonek, Schmidt & Vöckler GbR Cover design: deblik, Berlin Printed on acid-free paper

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About IFMBE The International Federation for Medical and Biological Engineering (IFMBE) was established in 1959 to provide medical and biological engineering with a vehicle for international collaboration in research and practice of the profession. The Federation has a long history of encouraging and promoting international cooperation and collaboration in the use of science and engineering for improving health and quality of life. The IFMBE is an organization with membership of national and transnational societies and an International Academy. At present there are 52 national members and 5 transnational members representing a total membership in excess of 120 000 worldwide. An observer category is provided to groups or organizations considering formal affiliation. Personal membership is possible for individuals living in countries without a member society The International Academy includes individuals who have been recognized by the IFMBE for their outstanding contributions to biomedical engineering.

Objectives The objectives of the International Federation for Medical and Biological Engineering are scientific, technological, literary, and educational. Within the field of medical, clinical and biological engineering it’s aims are to encourage research and the application of knowledge, and to disseminate information and promote collaboration. In pursuit of these aims the Federation engages in the following activities: sponsorship of national and international meetings, publication of official journals, cooperation with other societies and organizations, appointment of commissions on special problems, awarding of prizes and distinctions, establishment of professional standards and ethics within the field, as well as other activities which in the opinion of the General Assembly or the Administrative Council would further the cause of medical, clinical or biological engineering. It promotes the formation of regional, national, international or specialized societies, groups or boards, the coordination of bibliographic or informational services and the improvement of standards in terminology, equipment, methods and safety practices, and the delivery of health care. The Federation works to promote improved communication and understanding in the world community of engineering, medicine and biology.

Activities Publications of IFMBE include: the journal Medical and Biological Engineering and Computing, the electronic magazine IFMBE News, and the Book Series on Biomedical Engineering. In cooperation with its international and regional conferences, IFMBE also publishes the IFMBE Proceedings Series. All publications of the IFMBE are published by Springer Verlag. The Federation has two divisions: Clinical Engineering and Health Care Technology Assessment. Every three years the IFMBE holds a World Congress on Medical Physics and Biomedical Engineering, organized in cooperation with the IOMP and the IUPESM. In addition, annual, milestone and regional conferences are organized in different regions of the world, such as Asia Pacific, Europe, the Nordic-Baltic and Mediterranean regions, Africa and Latin America. The administrative council of the IFMBE meets once a year and is the steering body for the IFMBE: The council is subject to the rulings of the General Assembly, which meets every three years. Information on the activities of the IFMBE can be found on the web site at: http://www.ifmbe.org.

Foreword It is our great pleasure to welcome you at the11th Mediterranean Conference on Medical and Biological Engineering and Computing – the MEDICON 2007. After 24 years MEDICON conference is coming back to Slovenia, this time into its capital – Ljubljana. The MEDICON conferences are international events of high scientific standards with long lasting tradition held every third year in one of the Mediterranean countries under the auspices of the International Federation for Medical and Biological Engineering. Biomedical engineering today is a well-recognized area of research. It brings together bright minds from diverse disciplines ranging from engineering, physics, and computer sciences on one side to biology and medicine on the other side. With valuable assistance of members of the International Advisory Committee and Scientific Program Committee, the coorganizing institutions and societies, our sponsors, and distinguished invited lecturers we will ensure that the research and development presented at MEDICON 2007 plenary meetings, scientific sessions, and workshops will truly be relevant and up-to-date. MEDICON 2007 is taking place at the premises of University of Ljubljana, Faculty of Electrical Engineering. The choice was obvious: Ljubljana is accessible and vivid, and the academic environment is familiar to all of us. As Ljubljana is located in the centre of the country it offers an excellent opportunity also to explore on your own the remarkable variety of Slovenia’s scenery and national heritage. The Mediterranean coast of Primorska, Alpine resorts of Gorenjska, lovely hills and primeval forests of Dolenjska and the far-reaching plains of Prekmurje are just few examples of regional diversity you can encounter here. All these and other regions of Slovenia are easily accessible from Ljubljana within two hours of driving. We will visit some of our jewels together: Bled and Postojna. We feel confident that you will enjoy MEDICON 2007 both scientifically and socially. We will make every effort to make MEDICON 2007 a memorable event. This is also the place where you will meet your old friends and make new ones. Together we will all face new challenges imposed to us by our society and new technologies. We look forward to meeting you all in Ljubljana. Professor Damijan Miklavcic Organizing Committee The Chairman

Professor Tadej Bajd Slovenian Society of Medical and Biological Engineering, The President

Conference details Name: 11th Mediterranean Conference on Medical and Biological Engineering and Computing Short name: MEDICON 2007 Venue: Ljubljana, SLOVENIA June 26–30, 2007

Proceedings editors Tomaz Jarm Peter Kramar Anze Zupanic

Organized by Slovenian Society for Medical and Biological Engineering

In cooperation with: University of Ljubljana, Faculty of Electrical Engineering http://www.fe.uni-lj.si/ Institute for Rehabilitation of Republic of Slovenia http://www.ir-rs.si/ IFMBE - International Federation for Medical and Biological Engineering http://www.ifmbe.org Institute of Oncology, Ljubljana, Slovenia http://www.onko-i.si/ Jozef Stefan Institute, Ljubljana, Slovenia http://www.ijs.si/ijsw Clinical Center Ljubljana, Slovenia http://www2.kclj.si/ European Federation of Organisations for Medical Physics http://www.efomp.org/ University of Ljubljana, Faculty of Computer and Information Science http://www.fri.uni-lj.si/

University of Maribor, Faculty of Electrical Engineering and Computer Science http://www.feri.uni-mb.si/ podrocje.aspx

Local Organizing Committee Damijan Miklavcic (Chairman) Tadej Bajd Imre Cikajlo Peter Gajsek Tomaz Jarm Tadej Kotnik Peter Kramar Zlatko Matjacic Matjaz Mihelj Anze Zupanic Robert Cugelj Jadran Lenarcic Sasa Markovic Zvonimir Rudolf Tomaz Slivnik Igor Ticar

Scientific Programme Committee Maria Teresa Arredondo (Spain) Janez Bester (Slovenia) Manfred Bijak (Austria) Wolfgang Birkfellner (Austria) Bozidar Casar (Slovenia) Stelios Christofides (Greece) Igor Emri (Slovenia) Carlo Frigo (Italy) Borut Gersak (Slovenia) Milan Gregoric (Slovenia) Francis X. Hart (USA) William Harwin (UK) Ales Iglic (Slovenia) Paolo Inchingolo (Italy) Franc Jager (Slovenia) Joze Jelenc (Slovenia) Rihard Karba (Slovenia) Nada Lavrac (Slovenia) Ratko Magjarevic (Croatia) Crt Marincek (Slovenia)

Roberto Merletti (Italy) Lluis M. Mir (France) Marko Munih (Slovenia) Mustapha Nadi (France) Joachim Nagel (Germany) Eberhard Neumann (Germany) Franjo Pernus (Slovenia) Dejan Popovic (Denmark) Robert Riener (Switzerland) Gregor Sersa (Slovenia) Franc Solina (Slovenia) Vlado Stankovski (Slovenia) Martin Stefancic (Slovenia) Vojko Strojnik (Slovenia) Johannes Struijk (Denmark) Pascal Verdonck (Belgium) Max A. Viergever (Netherlands) Veljko Vlaisavljevic (Slovenia) Damjan Zazula (Slovenia) Ales Zemva (Slovenia) Tatjana Zrimec (Australia) Anton Zupan (Slovenia) Blaz Zupan (Slovenia)

International Advisory Committee Marcello Bracale (Italy) Ivan Bratko (Slovenia) Mario Cifrek (Croatia) David Elad (Israel) Attilio Evangelisti(Italy) Frederique Frouin (France) Enrique J Gomez (Spain) Akos Jobbagy(Hungary) Prodromos Kaplanis (Cyprus) Nicolas Pallikarakis (Greece) Costantinos S. Pattichis (Cyprus) Laura M. Roa (Spain) Herve Saint-Jalmes (France) Mario Forjaz Secca (Portugal) Thomas Sinkjaer (Denmark) Vesna Spasic Jokic (Serbia) Stanko Tonkovic (Croatia) Jos van der Sloten (Belgium) Peter Veltink (Netherlands)

IFMBE Mediterranean Conferences on Medical and Biological Engineering 1977–2007 MEDICON 1977 – I Mediterranean Conference on Medical and Biological Engineering, 12–17 September 1977, Sorrento, Italy MEDICON 1980 – II Mediterranean Conference on Medical and Biological Engineering, 15–19 September 1980, Marseilles, France MEDICON 1983 – III Mediterranean Conference on Medical and Biological Engineering, 5–9 September 1983, Portoroz, Yugoslavia MEDICON 1986 – IV Mediterranean Conference on Medical and Biological Engineering, 9–12 September 1986, Seville, Spain MEDICON 1989 – V Mediterranean Conference on Medical and Biological Engineering, 29 August–1 September 1989, Patras, Greece MEDICON 1992 – VI Mediterranean Conference on Medical and Biological Engineering, 5–10 July 1992, Capri, Italy MEDICON 1995 – VII Mediterranean Conference on Medical & Biological Engineering, 17–21 September 1995, Jerusalem, Israel MEDICON 1998 – VII Mediterranean Conference on Medical & Biological Engineering, 14–17 June 1998, Limassol, Cyprus MEDICON 2001 – IX Mediterranean Conference on Medical Engineering and Computing, 12–15 June 2001, Pula, Croatia MEDICON and HEALTH TELEMATICS 2004, X Mediterranean Conference on Medical and Biological Engineering, 31 July–5 August 2004, Ischia, Italy MEDICON 2007 – XI Mediterranean Conference on Medical Engineering and Computing June 26–30 2007, Ljubljana, Slovenia

Content Invited Lectures EMITEL – an e-Encyclopedia for Medical Imaging Technology......................................................................................... 1 S. Tabakov, C. A. Lewis, A. Cvetkov, M. Stoeva, EMITEL Consortium

Control for Therapeutic Functional Electrical Stimulation.................................................................................................. 3 Dejan B. Popovic, Mirjana B. Popovic

Patient-Cooperative Rehabilitation Robotics in Zurich ........................................................................................................ 7 Robert Riener

From Academy to Industry: Translational Research in Biophysics .................................................................................. 10 R. Cadossi, M.D

Information Technology Solutions for Diabetes management and prevention Current Challenges and Future Research directions............................................................................................................................................. 14 R. Bellazzi

Systemic Electroporation – Combining Electric Pulses with Bioactive Agents................................................................. 18 Eberhard Neumann,

Normal Sessions Analysis of ECG Intelligent Internet Based, High Quality ECG Analysis for Clinical Trials ...................................................................... 22 T.K. Zywietz, R. Fischer

Effects of vagal blockade on the complexity of heart rate variability in rats .................................................................... 26 M. Baumert, E. Nalivaiko and D. Abbott

Assessment of the Heart Rate Variability during Arousal from Sleep by Cohen’s Class Time-Frequency Distributions................................................................................................................. 30 M.O. Mendez , A.M. Bianchi , O.P. Villantieri and S. Cerutti

An algorithm for classification of ambulatory ECG leads according to type of transient ischemic episodes................. 34 A. Smrdel and F. Jager

Phase-Rectified Signal Averaging for the Detection of Quasi-Periodicities in Electrocardiogram ................................. 38 R. Schneider, A. Bauer, J.W. Kantelhardt, P. Barthel and G. Schmidt

Relative contribution of heart regions to the precordial ECG-an inverse computational approach .............................. 42 A.C. Linnenbank, A. van Oosterom, T.F. Oostendorp, P.F.H.M. van Dessel, A.C. van Rossum, R. Coronel, H.L. Tan, J.M.T. de Bakker

Classification Methods for Atrial Fibrillation Prediction after CABG.............................................................................. 46 S. Sovilj, R. Magjarević and G. Rajsman

Modelling effects of Sotalol on Action Potential morphology using a novel Markov model of the HERG channel.............................................................................................................................................................. 50 T.P. Brennan, M. Fink, B. Rodriguez, L.T. Tarassenko

Sample Entropy Analysis of Electrocardiograms to Characterize Recurrent Atrial Fibrillation................................... 54 R. Cervigon, C. Sanchez, J.M. Blas, R. Alcaraz, J. Mateo and J. Millet

USB Based ECG Acquisition System .................................................................................................................................... 58 J. Mihel, R. Magjarevic

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QT Intervals Are Prolonging Simultaneously with Increasing Heart Rate during Dynamical Experiment in Healthy Horses............................................................................................................... 62 P. Kozelek, J. Holcik

FPGA-based System for ECG Beat Detection and Classification ...................................................................................... 66 M. Cvikl and A. Zemva

Feature extraction and selection algorithms in biomedical data classifiers based on time-frequency and principle component analysis. ........................................................................................................................................ 70 P. S. Kostka, E. J. Tkacz

Dynamic Repolarization Assessment and Arrhythmic Risk Stratification........................................................................ 74 E. Pueyo, M. Malik and P. Laguna

Fractal analysis of heart rate variability in COPD patients................................................................................................ 78 G. D’Addio, A. Accardo, G. Corbi, N. Ferrara, F. Rengo

Autonomic Modulation of Ventricular Response by Exercise and Antiarrhythmic Drugs during Atrial Fibrillation ....................................................................................................................................................... 82 VDA Corino, LT Mainardi, D Husser, A Bollmann

Flexible Multichannel System for Bioelectrical Fields Analysis ......................................................................................... 86 P. Kneppo, M. Tysler, K. Hana, P. Smrcka, V. Rosik, S. Karas, E. Heblakova

Neural Networks Based Approach to remove Baseline drift in Biomedical Signals ......................................................... 90 J. Mateo, C. Sanchez, R. Alcaraz, C. Vaya and J. J. Rieta

Non-Linear Organization Analysis of the Dominant Atrial Frequency to Predict Spontaneous Termination of Atrial Fibrillation ............................................................................................................................................................... 94 R. Alcaraz and J. J. Rieta

Using Supervised Fuzzy Clustering and CWT for Ventricular Late Potentials (VLP) Detection in High-Resolution ECG Signal............................................................................................................................................. 99 Ayyoub Jafari, M.H. Morradi

Analysis of Surface EMG An Approach to the Real-Time Surface Electromyogram Decomposition...................................................................... 105 V. Glaser, A. Holobar and D. Zazula

Non-invasive estimation of the degree of motor unit synchronization in the biceps brachii muscle ............................ 109 A. Holobar, M. Gazzoni, D. Farina, D. Zazula, and R. Merletti

EMG Based Muscle Force Estimation using Motor Unit Twitch Model and Convolution Kernel Compensation...... 114 R. Istenic, A. Holobar , R. Merletti and D. Zazula

Model Based Decomposition of Muaps Into Their Constituent Sfeaps............................................................................ 118 M.G. Xyda, C.S. Pattichis, P. Kaplanis, C. Christodoulou and D. Zazula

Fast-Slow phase separation of Near InfraRed Spectroscopy to study Oxigenation v/s sEMG Changes....................... 124 Gian Carlo Filligoi

Analysis of Uterine EMG/EHG Uterine Electromyography in Humans – Contractions, Labor, and Delivery................................................................. 128 R. E. Garfield and W. L. Maner

Predictive value of EMG basal activity in the cervix at initiation of delivery in humans .............................................. 131 D. Rudel, G. Vidmar, B. Leskosek and I. Verdenik

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Evaluation of adaptive filtering methods on a 16 electrode electrohysterogram recorded externally in labor............ 135 J. Terrien, C. Marque, T. Steingrimsdottir and B. Karlsson

Abdominal EHG on a 4 by 4 grid: mapping and presenting the propagation of uterine contractions ......................... 139 B. Karlsson, J. Terrien, V. Gudmundsson, T. Steingrimsdottir and C. Marque

Evaluating Uterine Electrohysterogram with Entropy ..................................................................................................... 144 J. Vrhovec, A. Macek Lebar, D. Rudel

Detection of contractions during labour using the uterine electromyogram................................................................... 148 D. Novak, A. Macek-Lebar, D. Rudel and T. Jarm

Artificial Intelligence and Intelligent Data Analysis in Medicine GIFT: a tool for generating free text reports from encoded data..................................................................................... 152 Silvia Panzarasa, Silvana Quaglini, Mauro Pessina, Anna Cavallini, Giuseppe Micieli

Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data......................................................................................................................................... 157 N. Lavrac, P. Kralj, D. Gamberger and A. Krstacic

Availability Humanization - The Semantic Model in Occupational Health .................................................................... 162 M. Molan and G. Molan

Analyzing Distributed Medical Databases on DataMiningGrid© .................................................................................... 166 Vlado Stankovski, Martin Swain, Matevz Stimec and Natasa Fidler Mis

Bioimpedance FENOTIP: Microfluidics and Nanoelectrodes for the Electromagnetic Spectroscopy of Biological Cells ................... 170 V. Senez, A. Treizebré, E. Lennon, D. Legrand, H. Ghandour, B. Bocquet, T. Fujii and J. Mazurier

Impedance method for determination of the root canal length ........................................................................................ 174 D. Krizaj, J. Jan and T. Zagar

Separation of electroporated and non-electroporated cells by means of dielectrophoresis ........................................... 178 J. Oblak, D. Krizaj, S. Amon, A. Macek-Lebar and D. Miklavcic

A simple DAQ-card based bioimpedance measurement system....................................................................................... 182 T. Zagar and D. Krizaj

Bioimpedance spectroscopy of human blood at low frequency using coplanar microelectrodes .................................. 186 J. Prado, M. Nadi, C. Margo and A Rouane

Impedance Spectroscopy of Newt Tails .............................................................................................................................. 190 F.X. Hart, J.H. Johnson and N.J. Berner

Dielectric properties of water and blood samples with glucose at different concentrations .......................................... 194 A. Tura, S. Sbrignadello, S. Barison, S. Conti, G. Pacini

Parameter Optimization in Voltage Pulse Plethysmography............................................................................................ 198 M. Melinscak

Inherently Synchronous Data Acquisition as a Platform for Bioimpedance Measurement........................................... 202 G. Poola and J. Toomessoo

Benefits and disadvantages of impedance-ratio measuring method in new generation of apex-locators ..................... 206 T. Marjanovic, Z. Stare

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Biological Effects of Electromagnetic Radiation Effect of Modulated 450 MHz Microwave on HumanEEG at Different Field Power Densities .................................... 210 R. Tomson, H.Hinrikus, M. Bachmann, J. Lass, and V. Tuulik

Regenerative Effects of (-)-epigallocatechin-gallate Against Hepatic Oxidative Stress Resulted by Mobile Phone Exposure .................................................................................................................................................. 214 E. Ozgur, G. Güler and N. Seyhan

Conducting Implant in Low Frequency Electromagnetic Field ....................................................................................... 218 B. Valic, P. Gajsek and D.Miklavcic

Measurements of background electromagnetic fields in human environment................................................................ 222 T. Trcek, B. Valic and P. Gajsek

Numerical Assessment of Induced Current Densities for Pregnant Women Exposed to 50 Hz Electromagnetic Field............................................................................................................................................ 226 A. Zupanic, B. Valic and D. Miklavcic

The Relation Assessment Between 50 Hz Electric Field Exposure-Induced Protein Carbonyl Levels and The Protective Effect of Green Tea Catechin (EGCG) .............................................................................................. 230 A. Tomruk, G. Guler and N. Seyhan

EMF Monitoring Campaign in Slovenian Communes ...................................................................................................... 234 B. Valic, J. Jancar and P. Gajsek

Biomaterials Surface modification of titanium fiber-mesh scaffolds through a culture of human SAOS-2 osteoblasts electromagnetically stimulated ............................................................................................................................................ 238 L. Fassina, L. Visai, E. Saino, M.G. Cusella De Angelis, F. Benazzo and G. Magenes

Expression of Smooth Muscle Cells Grown on Magnesium Alloys .................................................................................. 242 S.K. Lu, W.H. Lee, T.Y. Tian, C.H. Chen, H.I. Yeh

Coalescence of phospholipid vesicles mediated by β2GPI – experiment and modelling ................................................ 246 J. Urbanija, B. Rozman, A. Iglič, T. Mareš, M. Daniel, Veronika Kralj-Iglič

Advancing in the quality of the cells assigned for Autologous Chondrocyte Implantation (ACI) method ................... 249 A. Barlic, D. Radosavljevic, M. Drobnic and N. Kregar-Velikonja

Mesenchymal Stem Cells: a Modern Approach to Treat Long Bones Defects................................................................ 253 H. Krečič-Stres, M. Krkovič, J. Koder, E. Maličev, M. Drobnič, D. Marolt and N. Kregar-Velikonja

Biomechanics Combination of microfluidic and structure-continual studies in biorheology of blood with magnetic additions ........ 257 E.Yu. Taran, V.A. Gryaznova and O.O. Melnyk

Virtual Rehabilitation of Lower Extremities...................................................................................................................... 262 T. Koritnik, T. Bajd and M. Munih

Rating Stroke Patients Based on Movement Analysis ....................................................................................................... 266 A. Jobbagy, G. Fazekas

Bending stiffness of odontoid fracture fixation with one cortical screw – numerical approach .................................... 270 L. Capek, P. Buchvald

Elastic Moduli and Poisson’s Ratios of Microscopic Human Femoral Trabeculae ........................................................ 274 J. Hong, H. Cha, Y. Park, S. Lee, G. Khang,and Y. Kim

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The Dissipation of Suction Waves in Flexible Tubes ......................................................................................................... 278 J. Feng and A.W. Khir

Hip stress distribution may be a risk factor for avascular necrosis of femoral head...................................................... 282 D. Dolinar, M. Ivanovski, I. List, M. Daniel, B. Mavcic, M. Tomsic, A. Iglic and V. Kralj-Iglic

Elasticity Distribution Imaging of Sliced Liver Cirrhosis and Hepatitis using a novel Tactile Mapping System........ 286 Y. Murayama, T. Yajima, H. Sakuma, Y. Hatakeyama, C.E. Constantinou, S. Takenoshita, S. Omata

Changes in Biomechanics Induced by Fatigue in Single-leg Jump and Landing............................................................ 288 J. Stublar, P. Usenik, R. Kamnik, M. Munih

Musculoskeletal Modeling to Provide Muscles and Ligaments Length Changes during Movement for Orthopaedic Surgery Planning...................................................................................................................................... 292 C.A. Frigo and E.E. Pavan

Numerical model of a myocyte for the evaluation of the influence of inotropic substances on the myocardial contractility............................................................................................................................................ 296 Bernardo Innocenti, Andrea Corvi

Biomechanical Analysis of Bolus Processing ...................................................................................................................... 300 T. Goldmann, S. Konvickova and L. Himmlova

Application of Simplified Ray Method for the Determination of the Cortical Bone Elastic Coefficients by the Ultrasonic Wave Inversion ....................................................................................................................................... 304 T. Goldmann, H. Seiner and M. Landa

Model for Muscle Force Calculation Including Dynamics Behavior and Vicoelastic Properties of Tendon................ 308 M. Vilimek

Biomedical Engineering Education and E-learning New courses in medical engineering, medical physics and bio/physics for clinical engineers, medicine and veterinary medicine specialists in Serbia..................................................................................................................... 310 V. M. Spasic-Jokic, D. Lj. Popovic, S. Stankovicand I. Z. Zupunski

The Education and Training of the Medical Physicist in Europe The European Federation of Organisations for Medical Physics -EFOMP Policy Statements and Efforts......................................................................................... 313 S. Christofides, T. Eudaldo, K. J. Olsen, J. H. Armas, R. Padovani, A. Del Guerra, W. Schlegel, M. Buchgeister, P. F. Sharp

Assessment of a system developed for virtual teaching ..................................................................................................... 319 M.L.A.Botelho, D.F.Cunha, F.B.Mendonca and S.J.Calil

A Web-Based E-learning Application on Electrochemotherapy ...................................................................................... 323 S. Corovic, J. Bester, A. Kos, M. Papic and D. Miklavcic

The value of clinical simulation-based training.................................................................................................................. 327 Vesna Paver-Erzen, Matej Cimerman

Biomedical Engineering and Virtual Education ................................................................................................................ 329 A. Kybartaite, J. Nousiainen, K. Lindroos, J. Malmivuo

Presentation of Cochlear Implant to Deaf People .............................................................................................................. 332 J. Vrhovec, A. Macek Lebar , D. Miklavcic, M. Eljon and J. Bester

Internet Examination – A New Tool in e-Learning ........................................................................................................... 336 J.A. Malmivuo, K. Lindroos and J.O. Nousiainen

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Biomedical Instrumentation and Measurement Development of a calibration bath for clinical thermometers .......................................................................................... 338 I. Pusnik, J. Bojkovski and J. Drnovsek

Evaluation of non-invasive blood pressure simulators ...................................................................................................... 342 G. Gersak and J. Drnovsek

Development of Implantable SAW Probe for Epilepsy Prediction .................................................................................. 346 N. Gopalsami, I. Osorio, S. Kulikov, S. Buyko, A. Martynov and A.C. Raptis

Accurate On-line Estimation of Delivered Dialysis Dose by Dialysis Adequacy Monitor (DIAMON) ......................... 350 I. Fridolin, J. Jerotskaja, K. Lauri, A.Scherbakovand M. Luman

Clinical implication of pulse wave analysis......................................................................................................................... 354 R. Accetto, K. Rener, J. Brguljan-Hitij, B. Salobir

Ambulatory blood pressure monitoring is highly sensitive for detection of early cardiovascular risk factors in young adults.......................................................................................................... 357 Maja Benca, Ales Zemva, Primoz Dolenc

Simple verification of infrared ear thermometers by use of fixed-point.......................................................................... 361 J. Bojkovski

Control Abilities of Power and Precision Grasping in Children of Different Ages ........................................................ 365 B. Bajd and L. Praprotnik

Bluetooth Portable Device for Continuous ECG and Patient Motion Monitoring During Daily Life .......................... 369 P. Bifulco, G. Gargiulo, M. Romano, A. Fratini and M. Cesarelli

Wearable Wireless Biopotential Electrode for ECG Monitoring ..................................................................................... 373 E.S. Valchinov and N.E. Pallikarakis

Modelling and Simulation of Ultrasound Non Linearities Measurement for Biological Mediums ............................... 377 R. Guelaz, D. Kourticheand M. Nadi

A Personal Computer as a Universal Controller for Medical-Focused Appliances........................................................ 381 Denis Pavliha, Matej Rebersek, Luka Krevs and Damijan Miklavcic

System Identification of Integrative Non Invasive Blood Pressure Sensor Based on ARMAX Estimator Algorithm ....................................................................................................................................... 385 Noaman M. Noaman, Abbas K. Abbas

Experimental Measurements of Potentials Generated by the Electrodes of a Cochlear Implant in a Phantom.......... 390 G. Tognola, A. Pesatori, M. Norgia, F. Sibella, S. Burdo, C. Svelto, M. Parazzini, A. Paglialonga, P. Ravazzani

Evaluation of muscle dynamic response measured before and after treatment of spastic muscle with a BTX-A − A case study ............................................................................................................................................... 393 D. Krizaj, K. Grabljevec, B. Simunic

Home Care Technologies for Ambient Assisted Living..................................................................................................... 397 Ratko Magjarevic

Development of the ISO standard for clinical thermometers ........................................................................................... 401 I. Pusnik

Hardware optimization of a Real-Time Telediagnosis System .......................................................................................... 405 Muhammad Kamrul Hasan, Md. Nazmus Sayadat, and Md. Atiqur Rahman Sarker

Application of time-gated, intensified CCD camera for imaging of absorption changes in non-homogenous medium. ............................................................................................................................................... 410 P. Sawosz, M. Kacprzak, A. Liebert, R. Maniewski

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The impact of the intubation model upon ventilation parameters ................................................................................... 413 B. Stankiewicz, J. Glapinski, M. Rawicz, B. Woloszczuk-Gebicka, M. Michnikowski, M. Darowski

The hybrid piston model of lungs ........................................................................................................................................ 416 M. Kozarski, K. Zielinski, K.J. Palko and M. Darowski

Biomedical Signal Processing Estimation of Neural Noise Spectrum in a Postural Control Model ................................................................................ 419 A.F. Kohn

Optimized Design of Single-sided Quadratic Phase Outer Volume Suppression Pulses for Magnetic Resonance Imaging ........................................................................................................................................ 423 N. Stikov, A. Mutapcic and J.M. Pauly

Analysis of foveation duration and repeatability at different gaze positions in patients affected by congenital nystagmus ...................................................................................................................................................... 426 M. Cesarelli, P. Bifulco, M. Romano, G. Pasquariello, A. Fratini, L. Loffredo, A. Magli, T . De Berardinis, D. Boccuzzi

Frequency characteristics of arterial catheters – an in vitro study .................................................................................. 430 F. T. Molnar and G. Halasz

Signal Processing methods for PPG Module to Increase Signal Quality ......................................................................... 434 K. Pilt, K. Meigas, J. Lass and M. Rosmann

Detection of the cancerous tissue sections in the breast optical biopsy dataflow using neural networks...................... 438 A. Nuzhny, S. Shumsky, T. Lyubynskaya

On the Occurrence of Phase-locked Pulse Train in the Peripheral Auditory System .................................................... 442 T. Matsuoka, D. Konno and M. Ogawa

A device for quantitative kinematic analysis of children’s handwriting movements...................................................... 445 A. Accardo, A. Chiap, M. Borean, L. Bravar, S. Zoia, M. Carrozzi and A. Scabar

Blood Flow and Oxygenation Measurement Monitoring of preterm infants during crying episodes...................................................................................................... 449 L. Bocchi, L. Spaccaterra, F. Favilli, L. Favilli, E. Atrei, C. Manfredi and G. P. Donzelli

Measuring Tumor Oxygenation by Electron Paramagnetic Resonance Oximetry in vivo............................................. 453 Z. Abramovic, M. Sentjurc and J. Kristl

Radiotracer and Microscopic Assessment of Vascular Function in Cancer Therapy .................................................... 457 G.M. Tozer and V.J. Cunningham

The Influence of Endurance Training on Brain and Leg Blood Volumes Translocation During an Orthostatic Test ............................................................................................................................................................... 461 A. Usaj

Comparison of two hypoxic markers: pimonidazole and glucose transporter 1 (Glut-1) .............................................. 465 A. Coer, M. Legan, D. Stiblar-Martincic, M. Cemazar, G. Sersa

Effects of vinblastine on blood flow of solid tumours in mice ........................................................................................... 469 S. Kranjc, T. Jarm, M. Cemazar, G. Sersa, A. Secerov, M. Auersperg

Automatic recognition of hemodynamic responses to rare stimuli using functional Near-Infrared Spectroscopy...... 473 M. Butti, A. C. Merzagora, M. Izzetoglu, S. Bunce, A. M. Bianchi, S. Cerutti, B. Onaral

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Brain Research and Analysis of EEG Brain on a Chip: Engineering Form and Function in Cultured Neuronal Networks..................................................... 477 B.C. Wheeler

Identification of Gripping-Force Control from Electroencephalographic Signals ......................................................... 478 A. Belic, B. Koritnik, V. Logar, S. Brezan, V. Rutar, R. Karba, G. Kurillo and J. Zidar

Quantitative EEG as a Diagnostic Tool in Patients with Head Injury and Posttraumatic Epilepsy............................. 482 T. Bojic, B. Ljesevic, A. Dragin, S. Jovic, L Schwirtlich, A. Stefanovic

BSI versus the Eye: EEG Monitoring in Carotid Endarterectomy.................................................................................. 487 W.A. Hofstraand M.J.A.M. van Putten

Assessing FSP Index Performance as an Objective MLAEP Detector during Stimulation at Several Sound Pressure Levels ........................................................................................................................................ 492 M. Cagy, A.F.C. Infantosi and E.J.B. Zaeyen

The Colorful Brain: Compact Visualisition of Routine EEG Recordings ....................................................................... 497 Michel J.A.M. van Putten

Using ANN on EEG signals to predict working memory task response .......................................................................... 501 V. Logar, A. Belic, B. Koritnik S. Brezan, V. Rutar, J. Zidar, R. Karba and D. Matko

Comparison of methods and co-registration maps of EEG and fMRI in Occipital Lobe Epilepsy............................... 505 M. Forjaz Secca, A. Leal, J. Cabraland H. Fernandes

Multimodal imaging issues for electric brain activity mapping in the presence of brain lesions .................................. 509 F. Vatta, P. Bruno, F. Di Salle, F. Meneghini, S. Mininel and P. Inchingolo

Proposal and validation of a framework for High Performance 3D True Electrical Brain Activity Mapping ............ 513 S. Mininel, P. Bruno, F. Meneghini, F. Vatta and P. Inchingolo

EEG Peak Alpha Frequency as an Indicator for Physical Fatigue .................................................................................. 517 S.C. Ng, P. Raveendran

Acetylcholine addition and electrical stimulation of dissociated neurons from an extended subthalamic area – A pilot study in the rat.......................................................................................................................................................... 521 T. Heida, K.G. Usunoff and E. Marani

Cross-correlation based methods for estimating the functional connectivity in populations of cortical neurons........ 525 A.N. Ide, M. Chiappalone , L. Berdondini , V. Sanguineti , S. Martinoia

Movement Related Potentials in Spontaneous and Provoked Thumb Movement .......................................................... 529 A.B. Sefer, M. Krbot, V. Isgum and M. Cifrek

Cardiovascular System Simulation of Renal Artery Stenosis Using Cardiovascular Electronic System.............................................................. 533 K.Hassani, M.Navidbakhsh and M.Rostami

Extracellular ATP-Purinoceptor Signaling for the Intercellular Synchronization of Intracellular Ca oscillation in Cultured Cardiac Myocytes.......................................................................................... 537 K. Kawahara and Y. Nakayama

Computer Assisted Optimization of Biventricular Pacing Assuming Ventricular Heterogeneity................................. 541 R. Miri, M. Reumann, D. Farina , B. Osswald, O. Dössel

Power density spectra of the velocity waveforms in Artificial heart valves..................................................................... 545 A. A. Sakhaeimanesh

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Medical Plans as a Middle Step in Building Heart Failure Expert System ..................................................................... 549 Alan Jovic, Marin Prcela and Goran Krstacic

Method for Reducing Pacing Current Threshold at Transesophageal Stimulation ....................................................... 554 A. Anier, J. Kaik and K. Meigas

User–centered system to manage Heart Failure in a mobile environment ...................................................................... 558 E. Villalba, D. Salvi, M. Ottaviano, I. Peinado, M. T. Arredondo, M. Docampo

Comparison of Four Calculation Techniques for Estimation of Local Arterial Compliance ........................................ 562 R. Raamat, J. Talts and K. Jagomägi

The Effect of in vitro Anticoagulant Disodium Citrate on Beta-2-glycoprotein I - Induced Coalescence of Giant Phospholipid Vesicles ............................................................................................................................................ 566 M. Frank, M. Lokar, J. Urbanija, M. Krzan, V. Kralj-Iglic, B. Rozman

Electroporation Based Therapies Cell membrane fluidity at different temperatures in relation to electroporation effectiveness of cell line V79 ........... 570 Masa Knaduser, Marjeta Sentjurcand Damijan Miklavcic

Voltage commutator for multiple electrodes in gene electrotransfer of skin cells .......................................................... 574 M. Kranjc, P. Kramar, M. Rebersek and D. Miklavcic

Voltage breakdown measurement of planar lipid bilayer mixtures ................................................................................. 578 P. Kramar, D. Miklavcic and A. Macek Lebar

Antitumor effectiveness of electrotransfer of p53 into murine sarcomas alone or combined with electrochemotherapy using cisplatin........................................................................................................................... 582 M. Cemazar, A. Grosel, S. Kranjc, and G. Sersa

Electrochemotherapy in veterinary medicine .................................................................................................................... 586 Natasa Tozon and Maja Cemazar

Tumor electrotransfection progress and prospects: the impact of knowledge about tumor histology ......................... 589 S. Mesojednik, D. Pavlin, G. Sersa, A. Coer, S. Kranjc, A. Grosel, G. Tevz, M. Cemazar

Quantification of ion transport during cell electroporation – theoretical and experimental analysis of transient and stable pores during cell electroporation.................................................................................................. 593 M. Pavlin, and D. Miklavcic

A numerical model of skin electroporation as a method to enhance gene transfection in skin...................................... 597 N. Pavselj, V. Preat and D. Miklavcic

Tumor blood flow modifying and vascular disrupting effect of electrochemotherapy................................................... 602 G. Sersa, M. Cemazar, S. Kranjc and D. Miklavcic

Real time electroporation control for accurate and safe in vivo electrogene therapy..................................................... 606 David Cukjati, Danute Batiuskaite, Damijan Miklavčič, Lluis M. Mir

Electrochemotherapy of equids cutaneous tumors: a 57 case retrospective study 1999-2005 ....................................... 610 Y. Tamzali, J. Teissie, M. Golzioand M. P. Rols

Electrochemotherapy in treatment of solid tumours in cancer patients .......................................................................... 614 G. Sersa for the ESOPE group

Electropulsation, an biophysical delivery method for therapy ......................................................................................... 618 J. Teissie and M. Cemazar

Bases and rationale of the electrochemotherapy................................................................................................................ 622 L.M. Mir

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A critical step in gene electrotransfer: the injection of the DNA...................................................................................... 623 F.M. André and L.M. Mir

In vivo imaging of siRNA electrotransfer and silencing in different organs ................................................................... 624 A. Paganin-Gioanni, J.M. Escoffre, L. Mazzolini, M.P. Rols, J. Teissiéand M. Golzio

An endoscopic system for gene & drug delivery directly to intraluminal tissue. ............................................................ 628 D.M. Soden, M. Sadadcharam, J. Piggott,A. Morrissey, C.G. Collins and G.C. O’Sullivan.

The effects of irreversible electroporation on tissue, in vivo............................................................................................. 629 Boris Rubinsky

Equine Cutaneous Tumors Treatment by Electro-chemo-immuno-geno-therapy ......................................................... 630 Y. Tamzali, B. Couderc, M.P. Rols, M. Golzio and J. Teissie

Analysis of Tissue Heating During Electroporation Based Therapy: A 3D FEM Model for a Pair of Needle Electrodes.............................................................................................................................................................. 631 I. Lackovic, R. Magjarevic and D. Miklavcic

The induced transmembrane potential and effective conductivity of cells in dense cell system .................................... 635 M. Pavlin, and D.Miklavcic

An experimental and numerical study of the induced transmembrane voltage and electroporation on clusters of irregularly shaped cells ................................................................................................................................. 639 G. Pucihar, T. Kotnik, and D. Miklavcic

Functional Electrical and Magnetic Stimulation The effect of afferent training on long-term neuroplastic changes in the human cerebral cortex ................................ 643 R.L.J. Meesen, O. Levin and S.P. Swinnen

An Experimental Test of Fuzzy Controller Based on Cycle-to-Cycle Control for FES-induced Gait: Knee Joint Control with Neurologically Intact Subjects................................................................................................... 647 T. Watanabe, A. Arifin, T. Masuko and M. Yoshizawa

Troubleshooting for DBS patients by a non-invasive method with subsequent examination of the implantable device...................................................................................................................................................... 651 H. Lanmüller, J. Wernisch and F. Alesch

Treating drop-foot in hemiplegics: the role of matrix electrode....................................................................................... 654 C. Azevedo-Coste, G. Bijelic, L. Schwirtlich and D.B. Popovic

FES treatment of lower extremities of patients with upper / lower motor neuron lesion: A comparison of rehabilitation strategies and stimulation equipment.................................................................................. 658 M. Bijak, M. Mödlin, C. Hofer, M. Rakos, H. Kern, W. Mayr

Optimal Control of Walking with Functional Electrical Stimulation: Inclusion of Physiological Constraints............ 661 Strahinja Dosen, Dejan B. Popovic

Magnetic Coils Design for Localized Stimulation .............................................................................................................. 665 L. Cret, M. Plesa, D. Stet and R.V. Ciupa

Gait and Motion Analysis Vertical unloading produced by electrically evoked withdrawal reflexes during gait: preliminary results................. 669 J. Emborg, E. Spaichand O.K. Andersen

Two-level control of bipedal walking model....................................................................................................................... 673 A. Olensek and Z. Matjacic

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Data mining time series of human locomotion data based on functional approximation............................................... 677 V. Ergovic, S. Tonkovic V. Medved and M. Kasovic

Kinematic and kinetic patterns of walking in spinal muscular atrophy, type III ........................................................... 681 Z. Matjacic, A. Praznikar, A. Olensek, J. Krajnik, I. Tomsic, M. Gorisek-Humar, A. Klemen, A. Zupan

The Gait E-Book – Development of Effective Participatory Learning using Simulation and Active Electronic Books ................................................................................................................................................ 685 A. Sandholm, P. Fritzson, V. Arora, Scott Delp, G. Petersson and J. Rose

A Study on Sensing System of Lower Limb Condition with Piezoelectric Gyroscopes: Measurements of Joint Angles and Gait Phases................................................................................................................. 689 Norio Furuse and Takashi Watanabe

Health Care and Medical Informatics A standard tool to interconnect clinical, genomic and proteomic data for personalization of cardiac disease treatment................................................................................................................................................. 693 M. Giacomini, F. Lorandi and C. Ruggiero

How do physicians make a decision?................................................................................................................................... 696 Kaiser Niknam, Mahdi Ghorbani Samini, Hedyeh Mahmudi, Sahar Niknam

Informational Internet-systems in Ukrainian healthcare – problems and perspectives................................................. 700 A.A. Lendyak

Reducing time in emergency medical service by improving information exchange among information systems........ 704 A. Jelovsek, M. Stern

Data Presentation Methods for Monitoring a Public Health-Care System ..................................................................... 708 Aleksander Pur, Marko Bohanec, Nada Lavrač, Bojan Cestnik

Adaptive Altered Auditory Feedback (AAF) device based on a multimodal intelligent monitor to treat the permanent developmental stuttering (PDS): A critical proposal ............................................................................... 712 Manuel Prado, Laura M. Roa

Simulation in Medicine and Nursing – First Experiences in Simulation centre at Faculty of Health Sciences University of Maribor........................................................................................................................... 716 D. Micetic-Turk, M. Krizmaric, H. Blazun, N. Krcevski-Skvarc, A. Kozelj, P. Kokol, Š. Grmec, Z. Turk

Open Source in Health Care: a milestone toward the creation of an ICT-based pan-European health facility .......... 719 D. Dinevski, P. Inchingolo, I. Krajnc, P. Kokol

The Open Three Consortium: an open-source, full-service-based world-wide e-health initiative ................................ 723 P. Inchingolo, M. Beltrame, P. Bosazzi, D. Dinevski G. Faustini, S. Mininel, A. Poli, F. Vatta

O3-RWS: a Java-based, IHE-compliant open-source radiology workstation ................................................................. 727 G. Faustini, P. Inchingolo

O3-DPACS: a Java-based, IHE compliant open-source data and image manager and archiver.................................. 732 M. Beltrame, P. Bosazzi, A. Poli, P. Inchingolo

GATEWAY: Assistive Technology for Education and Employment............................................................................... 737 D. Kervina, M. Jenko, M. Pustisek and J. Bester

Reshaping Clinical Trial Data Collection Process to Use the Advantages of the Web-Based Electronic Data Collection...................................................................................................................................................................... 741 I. Pavlovic and I. Lazarevic

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Telepathology: Success or Failure? ..................................................................................................................................... 745 D. Giansanti, L. Castrichella and M. R. Giovagnoli

E-learning for Laurea in Biomedical laboratory Technicians: a feasibility study .......................................................... 749 D. Giansanti, L. Castrichella and M.R. Giovagnoli

Health Care Technology Assessment and Management A hospital structural and technological performance indicators set................................................................................ 752 E. Iadanza, F. Dori and G. Biffi Gentili, G. Calani, E. Marini, E. Sladoievich, A. Surace

Continuous EEG monitoring in the Intensive Care Unit: Beta Scientific and Management Scientific aspects ........... 756 P.M.H. Sanders, M.J.A.M. van Putten

Technology Assessment for evaluating integration of Ambulatory Follow-up and Home Monitoring......................... 758 L. Pecchia, L. Bisaccia, P. Melillo, L. Argenziano, M. Bracale

A Multi Scale Methodology for Technology Assessment. A case study on Spine Surgery ............................................. 762 L. Pecchia, F. Acampora and S. Acampora, M. Bracale

Heart Rate Analysis Complexity Analysis of Heart Rate Control Using Symbolic Dynamics in Young Diabetic Patients ........................... 766 M. Javorka, Z. Trunkvalterova, I. Tonhajzerova, J. Javorkova and K. Javorka

Recurrence Quantification Analysis of Heart Rate Dynamics in Young Patients with Diabetes Mellitus.................... 769 Z. Trunkvalterova, M. Javorka, I. Tonhajzerova, J. Javorkova, and K. Javorka

Joint Symbolic Dynamic of Cardiovascular Time Series of Rats ..................................................................................... 773 D. Varga,T. Loncar-Turukalo, D.Bajic, S. Milutinovic, N. Japundzic-Zigon

Technical problems in STV indexes application ................................................................................................................ 777 M. Cesarelli, M. Romano, P. Bifulco

2CTG2: A new system for the antepartum analysis of fetal heart rate............................................................................ 781 G. Magenes, M.G. Signorini, M. Ferrario, F. Lunghi

Speeding up the Computation of Approximate Entropy................................................................................................... 785 G. Manis and S. Nikolopoulos

Cardiac arrhythmias and artifacts in fetal heart rate signals: detection and correction ............................................... 789 M. Cesarelli, M. Romano, P. Bifulco, A. Fratini

Medical Imaging Artery movement tracking in angiographic sequences for coronary flow calculation ................................................... 793 Hanna Goszczynska

Battery powered and wireless Electrical Impedance Tomography Spectroscopy Imaging using Bluetooth................ 798 A.L. McEwan and D.S. Holder

Using Heuristics for the Lung Fields Segmentation in Chest Radiographs..................................................................... 802 D. Gados and G. Horvath

Sampling Considerations and Resolution Enhancement in Ideal Planar Coded Aperture Nuclear Medicine Imaging ................................................................................................................................................................. 806 D.M. Starfield, D.M. Rubin and T. Marwala

Measuring Red Blood Cell Velocity with a Keyhole Tracking Algorithm....................................................................... 810 C.C. Reyes-Aldasoro, S. Akerman and G.M. Tozer

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Web-based Visualization Interface for Knee Cartilage..................................................................................................... 814 C.-L. Poh, R.I. Kitney and R.B.K. Shrestha

Markov Chain Based Edge Detection Algorithm for Evaluation of Capillary Microscopic Images............................. 818 G. Hamar, G. Horvath, Zs. Tarjan and T. Virag

Lung Surface Classification on High-Resolution CT using Machine Learning .............................................................. 822 S. Busayarat and T. Zrimec

Evaluation of Tomographic Reconstruction for Small Animals using micro Digital Tomosynthesis (microDTS)............................................................................................................................................................................ 826 D. Soimu, Z. Kamarianakis and N. Pallikarakis

Methods for Automatic Honeycombing Detection in HRCT images of the Lung........................................................... 830 T. Zrimec and J. Wong

Stochastic Rank Correlation - A novel merit function for dual energy 2D/3D registration in image-modulated radiation therapy .................................................................................................................................................................. 834 W. Birkfellner

Evaluation of peptides tagged nanoparticle adhesion to activated endothelial cells....................................................... 835 K. Rhee, H.J.Moon, K.S. Park and G. Khang

Estimation method for brain activities are influenced by blood pulsation effect............................................................ 839 W. H. Lee, J. H. Ku, H. R. Lee, K. W. Han, J. S. Park, J. J. Kim, I. Y. Kim, and S. I. Kim

Classification of Prostatic Tissues using Feature Selection Methods ............................................................................... 843 S. Bouatmane, B. Nekhoul, A. Bouridane and C. Tanougast

Texture Classification of Retinal Layers in Optical Coherence Tomography................................................................. 847 M. Baroni, S. Diciotti , A. Evangelisti, P. Fortunato and A. La Torre

Automatic cell detection in phase-contrast images for evaluation of electroporation efficiency in vitro ................................................................................................................................... 851 Marko Usaj, Drago Torkar, Damijan Miklavcic

Medical Physics Scattered radiation spectrum analysis for the breast cancer diagnostics ........................................................................ 856 S.A. Belkov, G.G. Kochemasov, N.V. Maslov, S.V. Bondarenko, N.M. Shakhova, I.Yu. Pavlycheva, A. Rubenchik, U. Kasthuri, L.B. Da Silva

Laminar Axially Directed Blood Flow Promotes Blood Clot Dissolution: Mathematical Modeling Verified by MR Microscopy................................................................................................................................................................ 859 J. Vidmar, B. Grobelnik, U. Mikac, G. Tratar, A. Blinc and I. Sersa

Snoring and CT Imaging...................................................................................................................................................... 864 I. Fajdiga, A. Koren and L. Dolenc

Modulation of the beam intensity with wax filter compensators...................................................................................... 867 D. Grabec and P. Strojan

A Model of Flow Mechanical Properties of the Lung and Airways ................................................................................. 871 B. Kuraszkiewicz, T. Podsiadly-Marczykowska and M. Darowski

Standard versus 3D optimized MRI-based planning for uterine cervix cancer brachyradiotherapy – The Ljubljana experience .................................................................................................................................................... 875 R. Hudej, P. Petric, J. Burger

Problems faced after the transition from a film to a DDR Radiology Department ........................................................ 879 S.P. Spyrou,I. Gerogiannis, A.P. Stefanoyiannis, S. Skannavis, A. Kalaitzis, P.A. Kaplanis

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Verification of planned relative dose distribution for irradiation treatment technique using half-beams in the area of field abutment ................................................................................................................................................ 883 R. Hudej

Experimental verification of the calculated dose for Stereotactic Radiosurgery with specially designed white polystyrene phantom .......................................................................................................... 887 B. Casar, A. Sarvari

In vivo dosimetry with diodes in radiotherapy patients treated with four field box technique ..................................... 891 A. Strojnik

The Cavitational Potential of a Single-leaflet Virtual MHV: A Multi-Physics and Multiscale Modelling Approach ................................................................................................................................... 895 D. Rafiroiu, V. Díaz-Zuccarini, D.R. Hose, P.V. Lawford, A.J. Narracott, R.V. Ciupa

Wavelet-based quantitative evaluation of a digital density equalization technique in mammography ........................ 899 A.P. Stefanoyiannis, I. Gerogiannis, E. Efstathopoulos, S. Christofides, P.A. Kaplanis, A. Gouliamos

Interaction between charged membrane surfaces mediated by charged nanoparticles ................................................. 903 J. Pavlic, A. Iglic, V. Kralj-Iglic, K. Bohinc

Optical biopsy system for breast cancer diagnostics.......................................................................................................... 907 S.A.Belkov, G.G.Kochemasov, S.M.Kulikov, V.N.Novikov, U.Kasthuri, L.B. Da Silva

Implantable brain microcooler for the closed-loop system of epileptic seizure prevention ........................................... 911 I. Osorio, G. Kochemasov, V. Baranov, V. Eroshenko, T. Lyubynskaya, N. Gopalsami

Acetabular forces and contact stresses in active abduction rehabilitation ...................................................................... 915 H. Debevec, A. Kristan, B. Mavcic, M. Cimerman, M. Tonin, V. Kralj-Iglic, and M. Daniel

Time-Frequency behaviour of the a-wave of the human electroretinogram ................................................................... 919 R. Barraco, L. Bellomonte and M. Brai

Studies on the attenuating properties of various materials used for protection in radiotherapy and their effect of on the dose distribution in rotational therapy..................................................................................... 923 T. Ivanova, G. Malatara, K. Bliznakova, D. Kardamakis and N. Pallikarakis

Monte Carlo Radiotherapy Simulator: Applications and Feasibility Studies ................................................................. 928 K. Bliznakova, D. Soimu, Z. Bliznakov and N. Pallikarakis

Recovery of 0,1 Hz microvascular skin blood flow in dysautonomic diabetic (type 2) neuropathy by using Frequency Rhythmic Electrical Modulation System (FREMS) ........................................................................ 932 M. Bevilacqua, M. Barrella, R. Toscano, A. Evangelisti

Rehabilitation Engineering Complementary evaluation tool for clinical instrument in post-stroke rehabilitation ................................................... 936 I.Cikajlo, M.Rudolf, N.Goljar and Z.Matjacic

Electrically Elicited Stapedius Muscle Reflex in Cochlear Implant System fitting ........................................................ 940 A. Wasowski , T. Palko , A. Lorens , A. Walkowiak , A. Obrycka , H. Skarzynski

Use of rapid prototyping technology in comprehensive rehabilitation of a patient with congenital facial deformity or partial finger or hand amputation ........................................................................... 943 T. Maver, H. Burger, N. Ihan Hren, A. Zuzek, L. Butolin, and J. Weingartner

Using computer vision in a rehabilitation method of a human hand ............................................................................... 947 J. Katrasnik, M. Veber and P. Peer

Experimental Evaluation of Training Device for Upper Extremities Sensory-Motor Ability Augmentation .............. 950 J. Perdan, R. Kamnik, P. Obreza, T. Bajd and M. Munih

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New Experimental Results in Assessing and Rehabilitating the Upper Limb Function by Means of the Grip Force Tracking Method.................................................................................................................................... 954 M.S. Poboroniuc, R. Kamnik, S. Ciprian, Gh. Livint, D. Lucache and T. Bajd

The “IRIS Home” ................................................................................................................................................................. 958 A. Zupan, R. Cugelj, F. Hocevar

Evaluation of biofeedback of abdominal muscles during exercise in COPD................................................................... 961 M. Tomsic

Robotics and Haptics Can haptic interface be used for evaluating upper limb prosthesis in children and adults .......................................... 965 H. Burger, D. Brezovar, S. Kotnik, A Bardorfer and M. Munih

FreeForm modeling of spinal implants ............................................................................................................................... 969 R.I. Campbell, M. Lo Sapio and M. Martorelli

Grip force response in graphical and haptic virtual environment ................................................................................... 973 J. Podobnik and M. Munih

A Hierarchical SOM to Identify and Recognize Objects in Sequences of Stereo Images............................................... 977 Giovanni Bertolini, Stefano Ramat, Member IEEE

Assessment of hand kinematics and its control in dexterous manipulation..................................................................... 982 M. Veber, T. Bajd and M. Munih

A model arm for testing motor control theories on corrective movements during reaching ......................................... 986 D. Curone, F. Lunghi, G. Magenes and S. Ramat

Sports Sessions Acceleration driven adaptive filter to remove motion artifact from EMG recordings in Whole Body Vibration ..................................................................................................................................................... 990 A.Fratini, M. Cesarelli, P. Bifulco, A. La Gatta, M. Romano, G. Pasquariello

The Influence of Reduced Breathing During Incremental Bicycle Exercise on Some Ventilatory and Gas Exchange Parameters ............................................................................................................................................ 994 J. Kapus, A. Usaj, V. Kapus, B. Strumbelj

A Novel Testing Tool for Balance in Sports and Rehabilitation....................................................................................... 998 N. Sarabon, G. Omejec

Change of mean frequency of EMG signal during 100 meter maximal free style swimming ..................................... 1002 I. Stirn, T. Vizintin, V. Kapus, T. Jarmand V. Strojnik

Telemonitoring of the step detection: toward two investigations based on different wearable sensors? ................... 1006 G. Maccioni, V. Macellari and D. Giansanti

Repeatability of the Mean Power Frequency of the Endurance Level During Fatiguing Isometric Muscle Contractions ........................................................................................................... 1009 I. Stirn, T. Jarmand V. Strojnik

Ultrasound Image Processing Obtaining completely stable cellular neural network templates for ultrasound image segmentation ........................ 1013 M. Lenic, D. Zazula and B. Cigale

Segmentation of 3D Ovarian Ultrasound Volumes using Continuous Wavelet Transform......................................... 1017 B. Cigale and D. Zazula

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Selected Applications of Dynamic Radiation Force of Ultrasound in Biomedicine ...................................................... 1021 A. Alizad, J.F. Greenleaf, and M. Fatemi

Breast Ultrasound Images Classification Using Morphometric Parameters Ordered by Mutual Information......... 1025 A.V. Alvarenga, J.L.R. Macrini, W.C.A. Pereira, C.E. Pedreira and A.F.C. Infantosi

Virtual Reality in Medicine Development of Knee Control Training System Using Virtual Reality for Hemiplegic Patients and Feasibility Experiment with Normal Participants.................................................................................................... 1030 J.S. Park, J.H. Ku, K.W. Han, S.W. Cho,D.Y. Kim, I.Y. Kim, and S.I. Kim

Development of Alcohol craving induction and measurement system using virtual reality: Craving characteristics to social situation ........................................................................................................................ 1034 S.W. Cho, J.H. Ku, J.S. Park, K.W. Han, Y.K. Choi, K. NamKoong, Y.C. Jung, J.J. Kim, I.Y. Kim, and S.I. Kim

Cataract Surgery Simulator for Medical Education ....................................................................................................... 1038 R. Barea, L. Boquete, J. F. Pérez, M. A. Dapena, P. Ramos, M. A. Hidalgo

Special Sessions and Symposiums Clinical Engineering and Patient Safety Patient safety - a challenge for clinical engineering ......................................................................................................... 1043 J.H. Nagel and M. Nagel

MIDS-project – a National Approach to Increase Patient Safety through Improved Use of Medical Information Data Systems.................................................................................................................................................. 1047 H. Terio

Improving Patient Safety Through Clinical Alarms Management ................................................................................ 1051 Y. David, J. Tobey Clark, J. Ott, T. Bauld, B. Patail, I. Gieras, M. Shepherd, S. Miodownik, J. Heyman, O. Keil, A. Lipschultz, B. Hyndman, W. Hyman, J. Keller, M. Baretich, W. Morse and D. Dickey

A Clinical Engineering Initiative within the Irish Healthcare System toward a Safer Patient Environment ............ 1055 P.J.C. Pentony, J. Mahady and R. Kinsella

A Pervasive Computing Approach in Medical Emergency Environments.................................................................... 1058 J. Thierry, C. Hafnerand S. Grasser

System for Tracing of blood transfusions and RFID ....................................................................................................... 1062 P. Di Giacomoand L. Bocchi

A preliminary setup model and protocol for checking electromagnetic interference between pacemakers and RFID (Radio Frequency IDentification).................................................................................................................... 1066 R. Tranfaglia, M. Bracale, A. Pone, L. Argenziano , L. Pecchia

Current Status of Clinical Engineering, Health Care Engineering and Health Care Technology Assessment in Austria ............................................................................................................................................................................. 1070 H. Gilly

Clinical Engineering Training Program in Emerging Countries Example from Albania ........................................... 1074 H. Terio

BME Education at the University of Trieste: the Higher Education in Clinical Engineering ..................................... 1077 P. Inchingolo and F. Vatta

Certification of Biomedical Engineering Technicians and Clinical Engineers: Important or Not.............................. 1081 James O. Wear, PhD, CCE, CHSP, FASHE, FAIMBE

Content

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Findings of the Worldwide Clinical Engineering Survey conducted by the Clinical Engineering Division of the International Federation for Medicine and Biological Engineering .................................................................... 1085 S.J. Calil, L.N. Nascimentoand F.R. Painter

Clinical Engineering in Malaysia – A Case Study............................................................................................................ 1089 Azman Hamid

Medical Equipment Inventorying and Installation of a Web-based Management System – Pilot Application in the Periphery of Crete, Greece ..................................................................................................... 1092 Z.B. Bliznakov, P.G. Malataras and N.E. Pallikarakis

A prototype device for thermo-hygrometric assessment of neonatal incubators .......................................................... 1096 P. Bifulco, M. Romano, A. Fratini, G. Pasquariello, and M. Cesarelli

Health Technology Assessment in Croatian Healthcare System .................................................................................... 1100 P. Milicic

A QFD-based approach to quality measurement in health care..................................................................................... 1102 F. Dori, E. Iadanza and D. Bottacci, S. Mattei

EVICAB - European Virtual Campus for Biomedical Engineering The E-HECE e-Learning Experience in BME Education............................................................................................... 1107 P. Inchingolo, F. Londero and F. Vatta

Web-based Supporting Material for Biomedical Engineering Education ..................................................................... 1111 K. Lindroos, J. Malmivuo, J. Nousiainen

European Virtual Campus for Biomedical Engineering EVICAB................................................................................. 1115 J.A. Malmivuo and J.O. Nousiainen

BIOMEDEA ........................................................................................................................................................................ 1118 Joachim H. Nagel

Biomedical Engineering Education, Virtual Campuses and the Bologna Process ........................................................ 1122 E.G. Salerud and Michail Ilias

How New and Evolving Biomedical Engineering Programs Benefit from EVICAB project....................................... 1126 A. Lukosevicius, V. Marozas

Learning Managements System as a Basis for Virtual Campus Project ....................................................................... 1130 K.V. Lindroos, M. Rajalakso and T. Väliharju

Future of Medical and Biological Engineering Computer Aided Surgery in The 21 Century ................................................................................................................... 1132 T. Dohi, K. Matsumiya and K. Masamune

Multi-dimensional fluorescence imaging .......................................................................................................................... 1134 P.M.W. French

Nanomedicine: Developing Nanotechnology for Applications in Medicine ................................................................... 1135 Gang Bao

The Physiome Project: A View of Integrative Biological Function ................................................................................ 1137 C.F. Dewey

Synthetic Biology – Engineering Biologically-based Devices and Systems .................................................................... 1138 R.I. Kitney

XXVIII

Content

Biomedical Engineering Clinical Innovations: Is the Past Prologue to the Future?..................................................... 1140 P. Citron

Innovations in Bioengineering Education for the 21 Century ........................................................................................ 1142 J.H. Linehan

Index Authors......................................................................................................................................1143 Index Subjects .....................................................................................................................................1149

Control for Therapeutic Functional Electrical Stimulation Dejan B. Popovic1,2, Mirjana B. Popovic1,2,3 1

Department of Health Science and Technology, SMI, Aalborg University 2 Faculty of Electrical Engineering, University of Belgrade 3 Center for Multidisciplinary Studies, University of Belgrade

Abstract— We suggest in this review paper that control of assistive systems for individuals with disability caused by injury or disease of central nervous system has to be approached with rather sophisticated methods that are capable to deal with high redundancy, nonlinearities, time variations, adaptation to the environment, and perturbations. The use of three levels that provide interaction with user, coordination of multi joint activity, and biological actuators is likely to be the solution for future electrical stimulation assistive systems. This is especially important for therapeutic assistive systems that must mimic life-like movement. The top control level needs to be discrete and secure the recognition of intended movement and possibly some kind of feedback, the middle control level needs to be discrete and provide multi joint coordination that is based on temporal and spatial synergistic model of the movement. The lowest control level needs to be model-based in order to match the specifics of the musculo-skeletal system. The hierarchical hybrid control is inherently predictive adaptive controller that, if properly designed, could results with effective generation of segment movements that lead to life like function (e.g., walking, standing, manipulation, grasping, etc.). Keywords— Hierarchical hybrid control, Electrical stimulation, Rule-based control, Model-based control.

I. INTRODUCTION Several clinical trials demonstrated that intensive taskoriented exercise augmented with an assistive system based on electrical stimulation promotes recovery of sensorymotor functions in individuals after central nervous system injury or disease [1,2]. The reasons most likely contributing to the recovery are: 1) assistive system contributes to nearnatural activity of the paretic extremity; thereby, play a part in the re-development of healthy-like movement. 2) proprioception and exteroception are enhanced due to artificially induced movement, 3) the electrical stimulation activates afferent pathways in parallel with activation of efferent pathways, 4) ability to perform function increases the motivation to use the paretic/paralyzed sensory-motor mechanisms, and 5) voluntary exercise prevents disuse and development of compensatory strategies. The physiological explanation of the recovery is that the central nervous system plasticity is augmented during the said therapy. Studies using Positron Emission Tomography

(PET), functional Magnetic Resonance Imaging (fMRI), transcranial magnetic stimulation (TMS), and magneto encephalography (MEG) support the concept of functional reorganization after CNS lesions [3,4]. II. CONTROL METHODS FOR THE THERAPEUTIC FUNCTIONAL ELECTRICAL STIMULATION

We start with the target of this research; that is, a model of biological control that needs to be mimicked (Fig. 1). Electrical stimulation activates muscles with bursts of pulses (electrical charge) delivered via electrodes exciting the intact peripheral nerves, which subsequently leads to generation of action potentials that propagate in both afferent and afferent pathways. The direct response of the stimulation will be the contraction producing joint torques, ultimately leading to movement; yet, a reflex component of movement will follow due to excitation of afferent pathways. One method to control the bursts of pulses is to apply model-based open-loop method. There is no correction that could be applied to the electrical stimulation in case of deviation of the produced movement from the desired one. The open-loop control performance was found unsatisfactory due to several reasons: parameter variation, inherent time-variance, time-delay, and strong nonlinearities in the musculo-skeletal system. The engineering method to deal with these problems is to introduce feedback, that is, to correct for errors by using sensory information that assesses the deviations of the trajectory from the desired one. The error-driven control ensures better tracking performance and smaller sensitivity to modeling errors, parameter variations, and external disturbances. There are many theoretical studies and some simple applications of closed-loop control system in FES; yet, none

Fig. 1: Model of control of movement in a human

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Dejan B. Popovic, Mirjana B. Popovic

has reached the maturity and found use in rehabilitation. The main problem is that the model of the system is far from reality, and that the parameters of the system can not be determined with necessary accuracy. The alternative method for control was suggested by Tomovic and McGhee [5] in form of a finite state control of multi-legged locomotion. This control method evaluated into the black box model, termed Rule Base Control (RBC), where the structure of the system was not considered; yet, only the inputs and outputs. The RBC is an open-loop control driven by sensory information that switches from rule to rule that had been developed through heuristics. The RRC in its nature is on-off control, meaning that it does not consider the dynamics of the system. Rules for RRC are derived by using computerized classification, typically some kind of artificial neural network. Artificial neural networks have been incorporated into the control schemes as they are able to learn complex nonlinear mappings [6, 7]. The stability issues remain unresolved due to the black-box structure. The development of this control method led to the development of hierarchical hybrid control (HHC) that in principle could lead to better performance [8]. The expanded schema of the model in Fig. 1 shows the elements that HHC is integrating (Fig. 2). III. HIERARCHICAL HYBRID CONTROL OF MOVEMENT Hybrid means, in general, heterogeneous in nature or composition. The term "hybrid systems" is understood to describe systems with behavior defined by entities or processes of distinct characteristics. The hybrid systems of interest here are dynamic systems where the behavior is determined by interacting continuous and discrete dynamics. These systems typically contain variables or signals that take values from a continuous set (e.g., the set of real numbers) and also variables that take values from a discrete and typically finite set (e.g., the set of symbols {a, b, c}). These continuous or discrete-valued variables or signals depend on independent variables such as time, which also may be continuous or discrete; some of the variables may also be a

Fig. 2: The organization of sensory-motor systems leading to movement.

discrete-event that is driven in an asynchronous manner. There are several reasons for using hybrid models to represent movement. Reducing the complexity was and still is an important reason for dealing with hybrid systems. This is accomplished in hybrid systems by incorporating models of dynamic processes at different levels of abstraction. For another example, in order to avoid dealing directly with a set of nonlinear equations, one may choose to work with sets of simpler equations (e.g., linear) and switch among these simpler models. This is a rather common approach in modeling physical phenomena. In control, switching among simple dynamical systems has been used successfully in practice for many decades. The hybrid control systems typically arise from the interaction of discrete planning algorithms and continuous processes, and, as such, they provide the basic framework and methodology for the analysis and synthesis of autonomous and intelligent systems, i.e., planning to move the hand and grasp an object. The hybrid control systems contain two distinct types of components, subsystems with continuous dynamics and subsystems with discrete-event dynamics that interact with each other. Another important way in which hybrid systems arise is from the hierarchical organization of complex control systems. In these systems, a hierarchical organization helps manage the complexity, and higher levels in the hierarchy require less detailed models (discrete abstractions) of the functioning of the lower levels, necessitating the interaction of discrete and continuous components. There are analogies between certain current approaches to hybrid control and digital control system methodologies. In digital control, one could carry the control design in the continuous-time domain, then approximate or emulate the controller by way of a discrete controller and implement it using an interface consisting of a sample and a hold device. Alternatively, one could first obtain a discrete model of the

Fig. 3: The model of life-like movement controller

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Control for Therapeutic Functional Electrical Stimulation

Fig. 4: The organization of a man made HHC for restoring movement plant taken together with the interface and then carry the controller design in the discrete domain. In hybrid systems, in a manner analogous to the latter case, one may obtain a discrete-event model of the plant together with the interface using automata or Petri nets. The schema of an HHC based controller for movement incorporates three levels (Fig. 3). The top artificial control structure is the interface between the user and the machine. This interface is the principal command channel, and it allows the user to trigger the operation of their choice volitionally. The actual organization of the controller is sketched in Fig. 4. The interface initiates the activity of a discrete, rule-base controller. This rule-base controller operates as a discrete, sample data feedback control, and its main role is to distribute the commands to the lowest actuator levels. The rulebase controller is implementing the finite-state model of movement, and the rules have to be determined with sufficient generality to allow the application over many assistive systems to be applicable to the entire population with a similar level of disability. The rule-based control (RBC) system is applicable for the coordination level of control (Fig. 5). This level deals with the following: 1) the strategy of how to employ the resources available, and 2) the methodology of how to maximize the efficiency of the resources. The RBC in this multilevel control uses the simulation of movement as the pattern that has to be followed. The RBC considers that the system is to be applied in individuals with disability; hence, the heuristics is applied to the output of simulation of optimal control applied to the model customized with parameters that reflect the level of impairment of the potential user and healthy-like tra-

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Fig. 5: The schema of a rule-based control for coordination level of hierarchical hybrid controller.

jectories [9]. The RBC comprises the following elements: the “regular” rules that are sequentially switching from one to the expected action based on sensory input; “mode” rules that are responsible for selecting the appropriate set of regular rules; and “hazard” rules that deal with conflict situations. The conflict situations occur due to the uncertainty of the available sensory information and/or hardware limitations, in addition to unexpected gait events. The hazard rules result in the safety “behavior”, which attempts to minimize the eventual catastrophic consequences of the hazard (e.g., falling, obstacles, non-physiological loading). The lower, actuator control level is responsible for executing decisions from the coordination level. The actuator level deals with specific muscle groups responsible for the flexion or the extension of a single joint, or in other cases, the action of several joints when a multiarticular muscle is externally stimulated. The actuator level implements the continuous feedback control and structural modeling. IV. DISCUSSION Compared with other classes of dynamical systems, the sensory-motor systems supporting the execution of functional motions in humans have three unique features: 1) they are highly redundant; 2) they are organized in a hierarchical structure, yet with many parallel channels; and 3) they are self-organized relying, among other things, on an extremely complex connectionism. In spite of the above complexity, the movements resulting from the action of sensory system, as a rule, are deterministic and they follow the preferred way of performing the intended motor task.

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The technology has spawned many different types of control system; each suited to a particular application. Over a much larger time scale, the biological organisms have evolved control systems to suit many of species and physiological functions. The two streams of development have converged in the area of motor control rehabilitation. This paper proposes the integration of these two control issues. In rehabilitation of movements the aim is to activate joints in a controlled way so as to restore as much motor function as possible in humans with motor disabilities. The control strategies implemented in most of rehabilitation devices have so far been fairly simple, and have been developed largely in relation to the design of machines rather than to the design of nervous systems. Recent neurophysiological data show that some of these strategies have converged, so as to be quite similar to those in analogous natural systems. The question now is, how general is this outcome? In artificial devices, should we always strive to mimic the relevant natural control system on the assumption that it has been optimized in the course of evolution, and could we always mimic nature, given that our abilities to reproduce components of the neuromuscular system are limited? The presented control is of specific interest for neurorehabilitation, that is, a method allowing the preserved structures to find their best use if appropriately trained. The intensive, task -oriented exercise is showing very positive recovery in individuals with disability (e.g., non-ambulating subjects can walk unassisted for some distances). Neural engineering is where the ultimate successes at this stage must come. The development of new sophisticated; yet, simple to use devices that interface the central and peripheral nervous system opens new horizons in rehabilitation. The recent MEMS based technology makes dramatic impact to the development of rehabilitation technology. This technology provides today sensing and actuation that has been difficult to imagine only months ago. The computer power rises fast, and allows the processing and communication at levels that are appropriate for the use in various implantable systems. The development of power sources is still somewhat limiting factor for implantable systems. The intelligent control that resembles to natural control is the major missing link being essential for full integration of the

Dejan B. Popovic, Mirjana B. Popovic

technology and life-sciences knowledge into viable, effective rehabilitation systems.

ACKNOWLEDGMENT Danish National Research Foundation, Copenhagen, Denmark, and Ministry for Science and Environmental Protection, Belgrade, Serbia partly supported this work.

REFERENCES 1.

2. 3. 4.

5. 6. 7. 8. 9.

Yan TB, Hui-Chan CWY, and Li LSW. (2005) Functional electrical stimulation improves motor recovery of the lower extremity and walking ability of subjects with first acute stroke - A randomized placebo-controlled trial, Stroke 36(1):80-5. Popovic MB, Popovic DB, Sinkjaer T, Stefanovic A and Schwirtlich L. (2003) Clinical Evaluation of Functional Electrical Therapy in Acute Hemiplegic Subjects. J Rehabil Res Dev 40(5):443-45 Khaslavskaya S and Sinkjær T. (2005) Motor cortex excitability following repetitive electrical stimulation of the common peroneal nerve depends on the voluntary drive. Exp Brain Res 162(4): 497-502 Ridding MC, Brouwer B, Miles TS, Pitcher JB, and Thompson PD. (2000) Changes in muscle responses to stimulation of the motor cortex induced by peripheral nerve stimulation in human subjects. Exp Brain Res 131(1):135-43 Tomovic R. and McGhee RB. (1968) A finite state approach to the synthesis of bioengineering control systems. IEEE Trans Human Factors Eng HFE-7:65-69 Kawato M, Furukawa K, and Suzuki R. (1987) A Hierarchical Neural-Network Model for Control and Learning of Voluntary Movement. Biol Cybern 57:169-185 Kawato M. (1990) Feedback-Error-Learning Neural Network for Supervised Motor Learning. Advanced Neural Computers, pp 365-372 Popovic MB. (2003) Control of Neural Prostheses for Grasping and Reaching. Med Eng Phys 25(1):41-50 Popovic DB. (2003) Control of Walking in Humans with Impact to Standing and Walking, J Aut Control, 13:1-34 Author: Dejan B. Popovic Institute: SMI, Aalborg University, Denmark (also University of Belgrade, Faculty of Electrical Engineering, Belgrade, Serbia) Street: Fredrik Bajers Vej 7D3 City: 9220 Aalborg Country: Denmark Email: [email protected]

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EMITEL – an e-Encyclopedia for Medical Imaging Technology S. Tabakov, C. A. Lewis, A. Cvetkov, M. Stoeva, EMITEL Consortium King’s College London, Dept. Medical Engineering and Physics, UK and EMITEL Consortium – www.emerald2.eu Abstract— The paper gives a brief explanation of a new International project EMITEL and its associated multilingual e-Dictionary. The project is developing the first web-based e-Encyclopedia in the profession. EMITEL will address the lifelong learning of a wide range of specialists and will be available free on Internet. The project advanced work-inprogress - the e-Dictionary is already functioning at www.emitdictionary.co.uk Keywords— Education and Training, e-Learning

I. INTRODUCTION Contemporary medicine is impossible without medical technology and medical imaging equipment is one of the most complex technologies of our time. Its effective and safe use requires well educated and trained work force Medical Physicists and Engineers. The field of medical imaging and related technologies develops rapidly. The last 20 years introduced revolutionary methods as Magnetic Resonance, Molecular Imaging, etc. All these enter quickly in healthcare and often limited information is available about new methods and respective technology. Previous projects, as EMERALD and EMIT, developed training materials (e-books and Image Databases) to address the initial training of young medical physicists. However most “mature” specialists (who already work in healthcare) do not have “the luxury” of free special training time to learn all these new methods and technologies. This places the “older” specialists in a disadvantaged position, as no suitable material is available for their timely lifelong education. The new project EMITEL aims to improve this situation by providing an Internet-based tool (Medical Imaging Technology e-Encyclopaedia for Lifelong Learning - EMITEL), which will allow flexible use of the limited time of these specialists to acquire knowledge about the newest developments in the field. The need of such material was discussed and assessed at the two International Conferences on Medical Physics/Engineering Education and Training (ICTP, Trieste, 1998, 2003) - described in the book ‘Towards a European Framework for Education and Training in Medical Physics and Biomedical Engineering’, IOS Press.

II. E-DICTIONARY The project was initiated some 5 years ago with an original multilingual Dictionary of Medical Imaging Technology Terms, which quickly grew to a full Medical Physics Dictionary cross-translating terms between each of its languages. Initially the e-Dictionary was CD-based and included English, French, German, Swedish and Italian. Later it was expanded with Spanish and Portuguese. The volume of the Dictionary is approx. 4000 terms (either a single word, e.g. dose, or combined words e.g. absorbed dose, or complex terms e.g. Linear dose-response curve, etc.). Through EMITEL the Dictionary was developed as a web-based tool. Also, it was further updated with Polish, Hungarian, Estonian, Lithuanian, Romanian and Turkish. The possibility to include and to search different alphabets allowed the Dictionary to further expand into Thai and another 5 languages, which are in preparation for inclusion during 2007. The original Search Engine of the Dictionary allows also partial word search and search of complex terms (i.e. a word anywhere in the term). One can see the advanced work in progress on the Dictionary at www.emitdictionary.co.uk where the interface of the web e-Dictionary is shown on Fig. 1. The EMITEL Consortium encourages contacts with all specialists who would like for their language to be included in the Dictionary, and also with colleagues who could suggest further omitted terms. III. EMITEL E-ENCYCLOPEDIA EMITEL task is to include explanatory articles to each of the 4000 terms. The articles will aim at MSc-level and above. Each article will include images, graphs, examples and other additional information. Typical size of an article is 150-300 words. The articles will be in English, but will be linked to the appropriate language in the Dictionary. The e-Encyclopedia will aim at more shorter articles, which will provide sufficient information for the term, together with related diagrams and practical figures. This departure from large topical articles will facilitate its Web use and multilingual term translation. At the moment the EMITEL Consortium includes 20+ specialists from King’s College London and King’s College

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Hospital, University of Lund and Lund University Hospital, University of Florence and AM Studio, Plovdiv. The Consortium includes also the IOMP (International Organisation for Medical Physics) as an international partner through which colleagues from other countries will be additionally included. After the end of the project the Web based eEncyclopedia EMITEL will be available free to all colleagues and IOMP will take care for its constant and quick future update.

S. Tabakov, C. A. Lewis, A. Cvetkov, M. Stoeva, EMITEL Consortium

IV. CONCLUSION The previous EU projects, carried out by the core of the present consortium, developed the world’s first structured training packages for young specialists in this field – EMERALD (focusing on X-ray, Nuclear Medicine and Radiotherapy) and EMIT (focusing on Ultrasound and Magnetic Resonance Imaging) [1]. These materials are now used in some 70 countries worldwide. As a result EMIT Consortium received the inaugural EU Award for Education – The Leonardo da Vinci Award (Maastricht, Dec. 2004). The new project EMITEL addresses the life long learning of a wider audience. It also includes Radiotherapy Physics, Radiation Protection and Hospital Safety terms, what makes it useful for various other healthcare specialists. Such product does not exist at the moment. In order to collect a wide feedback and include more colleagues into the project, EMITEL Consortium is organizing an International Conference of Experts during May 2008. Information on the development of the project can be found at its web site www.emerald2.eu. EMITEL builds another layer on the innovative developments of e-Learning in Medical Engineering and Physics, described in the Special Issue of the Journal of Medical Engineering and Physics. EMITEL Consortium [2] believes that this new web based tool will be a valuable contribution to the lifelong learning of many colleagues around the world and will help the development of the workforce in Medical Engineering and Physics.

ACKNOWLEDGMENT The authors express their gratitude to the EU Leonardo Programme and their own Institutions.

REFERENCES 1.

2.

Tabakov S, Roberts C., Jonsson B., Ljungberg M., Lewis C., Strand S., Lamm I., Milano F., Wirestam R., Simmons A., Deane C., Goss D., Aitken V., Noel A., Giraud J. (2005), Development of Educational Image Databases and e-books for Medical Physics Training, Journal Medical Engineering and Physics, Elsevier, vol.27, N.7, p 591-599. EMITEL Consortium members: S Tabakov, C A Lewis, C. Deane, D Goss, G Clarke, V Aitken, A. Simmons, S Keevil, J Coward, C. Deehan, P Smith, F Milano, S-E Strand, F. Stahlberg, B-A Jonsson, M Ljungberg, I-L Lamm, R. Wirestam, M. Almqvist, T Jonsson, A Cvetkov, M Stoeva Author: Dr Slavik Tabakov

Fig. 1 e-Dictionary interface (example from English-French Translation)

Institute: King’s College London, Dept. Medical Engineering and Physics Street: 124 Denmark Hill, London SE5 9RS,U.K. Email: [email protected]

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From Academy to Industry: Translational Research in Biophysics R. Cadossi, M.D1 1

Laboratory of Clinical Biophysics, IGEA, Carpi, Italy.

Abstract— The translation “from bench to bedside” of a scientific discovery, proof of principle or simple idea, that originated within academia, into a successful industrial product is a complex, long and costly process. Many factors need to be accounted for and careful planning and protection of intellectual property are essential to retain the value of the idea. This analysis, based on more than twenty-five years of experience in the biomedical field at IGEA, is presented to outline the different steps involved in such an endeavour. Critical factors defining the different phases, from initial evaluation of the idea to marketing and post-marketing monitoring, are described focusing on development processes. Keywords— Translational research, intellectual property, marketing, Cliniporator.

I. INTRODUCTION This analysis intends to illustrate how it is possible, starting from a scientific discovery, a proof of principle or a simple idea matured in the academic field, to successfully create and market an industrial product. Basis of such analysis is the hands-on experience gained by the IGEA company, that for over twenty-five years has been designing, manufacturing and marketing electromedical devices. The decision processes described below are used during the development of our products; as an example the focus will be on the experience collected for the development of Cliniporator. The Cliniporator project, conducted with the support of European Union research grants, has allowed IGEA to gather a large interdisciplinary international experience, teaching valuable lessons in what a translational research project entails. The phases that have been identified and explained below for an effective and optimized industrialization process are heavily influenced by the peculiarities of the medical field. New product development in the medical field is usually a long process that requires large and long term investments. Product validation and marketing follow rules that are specific to this area, further adding to the complexity of projects. Nevertheless, the steps described below can easily be adapted and put into effect for any field of industry.

II. INITIAL EVALUATION OF THE IDEA AND INTELLECTUAL PROPERTY PROTECTION

In the medical field, the value of an idea, and the reasons why one should develop such idea, reside in its ability to: a) improve the quality of care; b) reduce medical cost; c) simplify procedure; d) financial return and/or personal recognition. Thus, the suitability of an idea for industrial development should be evaluated on the basis of factors that involve different company departments: Finance Department, Marketing Department, R&D. The factors to be taken into consideration include: a) the originality and/or novelty of the idea; b) the innovative content of the final product with respect to those already on the market; c) results from market analysis; d) the price competitiveness of the final product with respect to products already on the market; e) the financial and human resources available in the company to support the project; f) the capacity to undertake the project on the basis of company know-how; g) the availability of human, material and logistic resources; h) analysis of the clinical validation process feasibility. Following this initial evaluation of the idea, steps should be taken to ensure adequate protection of intellectual property inherent in the idea and/or that will be developed during the project. Ways to protect intellectual properties are: a) follow the patent process b) correct use of Confidentiality Disclosure Agreement (CDA) and Nondisclosure Agreement (NDA). III. FEASIBILITY STUDY: DEFINITION OF A WORKABLE PROJECT

The R&D department undertakes the preliminary research activity to identify the state-of-the-art technological solutions available to develop the initial idea in an industrial context. This feasibility study, and where possible the presentation of an early prototype, allows the Marketing Department and the Finance Department to evaluate whether the workable project meets the initial idea and the purposes of the different company functions and therefore whether the industrial development of the idea is viable.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 10–13, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

From Academy to Industry: Translational Research in Biophysics

To obtain an effective feasibility study the R&D should: a) control and integrate the initial project specifics (the initial idea); b) research technical and manufacturing solutions; c) present the workable project, using sketches, diagrams or physical prototypes; d) present an initial evaluation of the cost of the final product. If the project needs to be developed with out of house expertise (i.e. subcontractors), care should be taken as to have some type of patent protection in place. Once all these steps have been successfully completed, R&D department will obtain the approval by Finance department to prepare a design plan. At this time the Project Leader will be identified. IV. DESIGN PLAN The Project Leader studies the work program and plans what will be the workflow of the project using a planning document that defines: a) specifics of the project; b) precise description of the tasks to be completed, and their assignment to appropriate workgroups c) the time plan for each task; d predefined control points to verify that the solutions developed are correct and compatible with the specifics of the design; e) the resources for the development of the project, as approved and made available by the Finance department; f) a contingency plan. Of note is the importance of good communication and coordination among different units participating in the program, be as they may different division within the same company or completely separate entities such as: other companies, research centers or hospitals. Such a condition will facilitate problem solving and drastically decrease delays due to miscommunication. V. VALIDATION OF A WORKING PROTOTYPE The Project Leader oversees and coordinates the workgroups involved in the programmed phases to obtain a working prototype, respecting the scheduled time frames and check points. As the project progresses the prototype will be subjected to functionality tests of the technology that is been developed. Specifically for electro-medical devices the prototypes will also be used to carry out scientific research that should include: in vitro and in vivo studies to characterize the mechanisms of action. In vivo study will also be a relevant part of the pre-clinical validation of the

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project. Final clinical validation of the device will be attained through clinical trials. VI. CLINICAL VALIDATION The Project Leader co-ordinates the scientific research necessary for the clinical validation of the developed technology. At this time it is extremely important to define the expected results and the parameters on the bases of which the efficacy of the treatment will be assessed. In the event of a negative outcome, one should consider either varying the specifics of the project or closing it down to limit financial and time losses. Provided the result of the clinical trial are positive, the technology cost/benefit ratio will be considered and compared to that of already existing alternative treatments to further assess viability and profitability of the project as a whole. VII. APPROVAL OF THE PROTOTYPE The Finance and Marketing Department and R&D evaluate the prototype whose technology has been positively tested and recognized as clinically valid. Indeed, it is necessary to select the characteristics to be included in the product, that should be manufactured and marketed. Finally, those specifications that have not yet been discussed should be defined, i.e. user interface, subsidiary functions and aesthetics. To carry out this evaluation the following are analyzed: a) features of the prototype; b) results of the functionality tests or of the scientific research; c) any new requirements identified by marketing; d) proposals for boxes, accessories and user interface; e) requisites for manufacturing. In this phase, considerations about localization of the product, such as interface language and cultural differences that could influence product acceptance and diffusion, are identified and implemented in the project. Once the prototype and the new additional specifics are approved, the development and marketing activity of the new product can be initiated. VIII. PREPARATION OF THE MARKETING STRATEGY The attainment of a “working” prototype, or rather the awareness of the performance of the new product and its particularities with respect to the competition or market alternatives, allows the Marketing Department to put together a precise strategy aimed at introducing the new product on the market.

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R. Cadossi, M.D

The Marketing Plan should be studied together with the Finance department, which provides the necessary resources, and with the Sales Management. For an effective marketing activity, it is necessary to carefully define: a) the target, i.e. the indications for use and who should use the product; b) the communication, i.e. the message; the instruments: brochures, leaflets, congresses, round tables, training centers, newspapers; c) the opinion leaders; d) the competition, i.e. the analysis of the competition, the alternatives to the new product; e) the resources, i.e. the investment needed for promotion, training and/or integration with the Sales Force; f) the price strategy. IX. IDENTIFICATION OF THE DEFINITIVE CONSTRUCTION SOLUTIONS

The R&D department develops the prototype, introducing modifications and integration to fulfill specification changes as determined during initial testing, or identified by the Marketing Department and choosing construction solutions suitable for manufacturing and which meet all the standards that apply to the product in question. To be able to develop the definitive solutions, effective coordination is needed between those working on the hardware, the software, the user interface, those writing the manuals and the forms and those working on obtaining the certification of the product. This co-ordination is managed and guaranteed by the Project Leader, who approves the solutions identified and transfers them to the Marketing Department that is responsible for the design of a precise and effective promotional strategy. The Project Leader also guarantees that every variation requested or proposed with respect to the already approved prototype is freshly discussed and approved by the Marketing Management. The product that is obtained by the development activity must meet the specifics of the project plan, the requisites defined subsequently and the standards defined in the different markets. The product is presented to the Marketing and Sales Division that, on the basis of the promotional strategy worked out define the “supplementary” aspects that are needed before sales begin: refining of the interface (colors, signs, messages); design of the labeling and packaging; commercial name, appearance and contents of the illustrative material. The approval of the final prototype, together with the definition of all the aspects necessary for manufacturing, gives the go ahead for the construction products for sale.

X. CERTIFICATIONS ATTAINEMENT AND TRANSFER TO MANUFACTURING

The R&D department co-ordinates the activities for the development of the first-series models and therefore: a) completes the certification process, completes the Technical File, the Device Master Record (DMR), and the Risk Management File (RMF); b) prepares the registration reports required by the compulsory norms and by the company Quality System; c) transfers to manufacturing the necessary data for the management of the components; d) transfers to manufacturing the know-how and the operational methods for the construction, testing and management of the product; e) co-ordinates the tests on the first-series models, necessary for the validation of the design and manufacturing process. The attainment of the certification, the transfer into manufacturing and the successful completion of the tests on the first-series models signals the end of the planning process. XI.

MARKETING ACTIVITY AND SALES STRATEGY

The start of the marketing activity, i.e. the preparation of the market for the introduction of the new product, together with its final development, provide the Sales Force with all the tools to start the detailed promotion in the field (Leaflets, Price Lists, Communication, Opinion Leaders). The Sales Force should prepare, together with the Finance and Marketing Department, a Sales Plan which defines: a) the priorities; b) the resources to be allocated to each objective; c) the return expected from operations; d) the contingency plan. XII. DISSEMINATION The members of the Marketing division who followed the development and validation of the product and who took care of the promotional strategy, the Core Group, select and prepare the Opinion Leaders in the countries where the new product will be introduced. In this sense the International Marketing Division creates, instructs, provides resources and tools for the National Marketing Divisions that have their references in the local Opinion Leaders. XIII. CUSTOMER CARE After the product has been marketed, Customer Care records every type of complaint, request for assistance and

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From Academy to Industry: Translational Research in Biophysics

feedback from the client. This information is monitored to identify problems and to initiate corrective action or make improvements to the product. Such solutions may involve the R&D process (updating the product), the manufacturing process and/or the promotional strategy. Any change to product needs to be approved by the Marketing and Sale Department before it is implemented in Products for sale. XIV. CONCLUSIONS The development of a product to be used to treat patients, a biomedical device, is certainly a long and costly process that involves all strategic functions of a company from the very onset of the project. The target and the potential diffusion of the new product have to be clearly identified since the beginning of the process. Moreover, peculiar to the development of a medical device is a further variable: i.e. the clinical validation whose outcome cannot be controlled. In fact, despite in vitro and in vivo results may be satisfactory, they do not necessarily guarantee that they can be translated into an effective clinical application. For example the above project steps have been followed in the successful experience we had in the Cliniporator project. Nevertheless we should not forget that from early experimental data on electrochemotherapy, that envisioned its use in clinical

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practice, to the introduction into the market of a CE marked device validated through the ESOPE clinical trial, 15 years have elapsed.

REFERENCES 1. 2. 3. 4. 5.

IGEA Quality Manual, Chapter 7.3: ISO 9001:2000. Bonutti PM, Seyler TM, Marker DR, Plate JF, Mont MA. Inventing Orthopedics: From Basic Design To Working Product. AAOS Annual Meeting Scientific Exhibit, San Diego, USA. 2007. Mir LM, Orlowski S, Belehradek J Jr, Paoletti C. Electrochemotherapy potentiation of antitumour effect of bleomycin by local electric pulses: Eur J Cancer. 1991;27(1):68-72. Lebar AM, Sersa G, Kranjc S, Groselj A, Miklavcic D. Optimisation of pulse parameters in vitro for in vivo electrochemotherapy: Anticancer Res. 2002 May-Jun;22(3):1731-6. Gothelf A, Mir LM, Gehl J. Electrochemotherapy: results of cancer treatment using enhanced delivery of bleomycin by electroporation: Cancer Treat Rev. 2003 Oct;29(5):371-87. Author: Ruggero Cadossi, M.D. Institute: Street: City: Country: Email:

Laboratory of Clinical Biophysics, IGEA Via Parmenide 10/A 41012 Carpi (MO) Italy [email protected]

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Information Technology Solutions for Diabetes management and prevention Current Challenges and Future Research directions R. Bellazzi1 1

Dipartimento di Informatica e Sistemistica, Universita di Pavia, Italy

Abstract— This paper presents a list of current and future challenges for Information technology in Diabetes Mellitus management. Three main research areas are identified: supporting patients, supporting health care providers, including specialists, GPs and case managers and finally supporting organizations and health care policy makers. The new technological advances in context awareness, user modelling, data mining and integrated information systems enable the design and implementation of new decision support strategies which may be effective in improving the management and prevention of chronic diseases in general and of Diabetes Mellitus in particular. Keywords— Diabetes Management, decision support, information and communication technologies

I. INTRODUCTION Diabetes Mellitus (DM), a major metabolic disease related with a reduced or impaired capability of the body to regulate the Blood Glucose Level, is now reaching worldwide epidemic proportions. The prevalence of DM for all age-groups was recently estimated to be 2.8% in 2000 and to reach 4.4% in 2030, so that the total number of people with DM, which is currently 171 million, is projected to raise to 366 million in 2030 [1]. The International Diabetes Federation estimates that the majority of patients is affected by type 2 Diabetes (DM-2), namely 85-95% of Diabetics, while about 0.09% of the population is affected by type 1 Diabetes (DM-1). Europe has the highest number of DM-1 patients (1.27 million) (http://www.idf.org). Given its epidemical proportion, it is not surprising that DM is having a strong impact on the health care system of western countries. DCCT and UKPDS studies [2,3] have shown that DM related complications, including cardiovascular diseases, can be delayed or prevented through a strict metabolic control. For this reason a number of guidelines and best-practice procedures have been defined to improve the delivery of care. Moreover, it has been recently demonstrated that DM2 can be delayed or prevented, too, by means of intensive interventions on diet, exercise or medication; such interventions have been also proved to be cost-effective [4]. The epidemiological dimension of DM-2 is forcing national health authorities to launch programs for the prevention,

early diagnosis and management of DM-2 based on informative campaigns targeted to citizens. There are however several problems in the implementation of such prevention and disease management programs, mainly related to a timely identification of citizens at risk of diabetes and cardiovascular diseases and then to the implementation of personalized life-style and clinical interventions. Nowadays it has become clear that the difficulties in achieving a satisfactory level in the delivery of health care to DM patients are not related to the availability of knowledge on the best diagnostic and therapeutic procedures, while they are system and organizational problems [5]. Over the last thirty years a strong interest has been given to the design and implementation of systems based on Information and Communication Technology (ICT) aimed at supporting the management of DM, mainly in the areas of Electronic patient records, Decision support systems and telemedicine [6-8]. In particular, diabetes care is probably one of the areas in which telemedicine, e-Health and Consumer-health solutions have been more widely tested [9-11]. The chronic nature of the disease and the need of patients empowerment for performing glucose self-monitoring and insulin delivery makes DM a “natural” context to test ICT as a mean for the provision of home care. Some of the proposed systems are now running large clinical trials, although very few of them became part of disease management programs, supporting multi-faceted interventions for patient care [10, 12]. In this paper we will describe some of the most important research areas which may enable the effective implementation of ICT solutions for DM and may thus provide very useful tools for improving the delivery of care. II. CHALLENGES AND RESEARCH ARCH DIRECTIONS FOR ICT IN DIABETES MANAGEMENT

A number of unparalleled opportunities are nowadays available to implement disease management and prevention programs based on the current advances of research in ICT: •

The availability of centralized electronic data repository on drug and specialized visit prescriptions and on the citizen hospital admissions and discharges. Such data

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Information Technology Solutions for Diabetes management and prevention Current Challenges and Future Research directions

• • •

are a crucial source of information to perform citizen stratification for DM and cardiovascular risk. The availability of new research results on DM risk assessment, coming from epidemiology and from genetics/genomics/proteomic research. The availability of new ICT solutions based on usercentered design, mobile communication and theory of behavior change. The improvement of research in the areas of context awareness and wearable systems.

Those advances enable the design of new ICT-based decision support systems in DM management devoted to support all the actors involved in DM management and prevention: patients, diabetologists, GPs and case managers, health care policy makers. A. Supporting patients Traditionally, decision support systems have been classified as visit-by-visit systems and day-by-day systems, the first being aimed at supporting physicians and the second to help DM patients in their self-management activities. The availability of telemedicine solutions have changed this kind of paradigm, potentially providing patients and physicians with the same kind of information about selfmonitoring, though with different roles and responsibilities. The majority of the past efforts have been devoted to the design of systems for insulin management. We believe that current research frontier is on the contrary related to the citizen and patient empowerment through user modeling and context awareness. In the area of user modeling, there has been a tremendous growth in diversity of ICT solutions for over the last two decades, with particular reference to lifestyle behavior change. These systems provide health behavior change information to citizens on the basis of a variety of health behavior theories [13,14], using different communication media, ranging from Web sites to computer telephony interfaces (CTI). From a clinical viewpoint they have been applied to a wide number of behaviors, including DM related ones, such as physical activity promotion, diet adherence, medication regimen adherence. Overall, these systems have been shown to be effective in a number of randomized clinical trials [15]. Recently, an interesting paper has been published by Ma and colleagues on the delivery of information and communication support to DM patient on the Internet [16]. The system is able to select patient-specific information, prioritizing diabetes learning topics and defining individualized agendas for patient-physician encounters on the basis of the so-called “Diabetes Information Profile” (DIP). The DIP is a model, i.e. a multi-faceted profile, of

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the user which is progressively updated on the basis of the clinical data and of the patient interaction with the system. The technology has been evaluated through a small clinical study, which showed its potential effectiveness in providing useful information to patients. Web interfaces can be also used to implement embodied conversational agents and relational agents. which are animated computer-based characters that emulate human faceto-face conversation [17,18]. Promising future directions also involve wearable computers, PDAs and mobile phones as platforms for health behavior change interventions [19]. Other solutions would be of interest in order to seamlessly integrate support into the users’ everyday lives and to initiate the interaction with the user, such as Short Message Service (SMS) or natural language interaction systems integrated within CTI. The availability of such technologies can help to better define the “context” in which the conversation between the user and the helper application occurs. By context we mean the “interrelated conditions in which something exists or occurs”, including anything known about the participants in the (potential) communication relationship. Therefore, context will regard not only all the user data and information collected during the dialog but also something related to the “presence concept”. The presence concept depicts availability to communicate but proposed extensions include elements to be derived from user activity, sensors or others. The knowledge about the user (user modeling) and the context (context awareness) in which the monitoring activity is carried on opens interesting research questions concerning the personalization and distribution of decision support interventions. B. Supporting specialists, GPs and case managers Several computer-based systems for Diabetes management have been proposed since the early eighties [20,21,22]. Many of the systems described in the literature have been primarily designed to manage DM-1 patients, in accordance to the “specialist-patient” model of health care; on the contrary, the current interests are directed towards the management of DM-2 within a “specialist-GP-patient” model. The current trend is to integrate guidelines and decision support systems as reminders within Electronic Patient Records (EPR) to support complex primary care interventions. The need of integrated solutions are advocated also by the substantial lack of clinical evidence that stand-alone guideline-augmented EPR may be effective in clinical practice [23]. As an interesting example of integrated system we can refer to the Diabetes Audit and Research in Tayside (DARTS), which is a validated population-based diabetes information system that collects data coming from different

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sources, including hospital admissions, diabetes clinical visits and diabetes medication. DARTS have been redesigned to overcome the problem of “inertia to change”, which is considered the main reason for the sporadic uptake and partial use of ICT-based systems. DARTS combines different technologies to allow for universal data collection, correlation, dissemination, guideline provision, guideline implementation and others. Currently DARTS is used in one thousand general practice clinic sites and fifty major hospital clinics, routinely managing a diabetic patient population of more than 160,000 patients [24]. Another relevant integrated ICT intervention is the IDEATel project, now funded for eight years since the year 2000. IDEATel is designed to provide a telemedicine service in both urban and rural economically disadvantaged areas within New York State. The project involved 1,500 patients, half of them being managed through a telemedicine intervention. Patients, GPs, case managers and specialists are connected by means of an Internet service; the telemedicine service is fully integrated with an health-care information system and is empowered by guideline based reminders and alerts. After the first year of implementation some improvements in the clinical outcomes have been observed, in particular regarding blood pressure and LDL [25]. Finally, an interesting research effort has been represented by the European Project M2DM: Multi-Access Services for Managing Diabetes Mellitus. The main goal of the project was to develop and test a Multi-Access service for managing all type of Diabetic patients. The basic concept is to collect data in a central database server that can be accessed through the Web, through the phone or through dedicated software for data downloading from the glucometers. The M2DM system comprised a Web access, a CTI service based on an Interactive Voice response system and a smartmodem located at home. The Web pages were optimized for different access modalities, including mobile devices. A distinguishing feature of M2DM is to exploit technology for managing the knowledge available to patients and physicians. To this end, the information flows is regulated by a scheduler, called Organizer that, on the basis of the knowledge on the health-care organization, it is able to automatically send e-mails and alerts as well as to commit activities to software agents, such as data analysis. Many decision support tools are integrated in the system, including casebased and rule-based reasoning, as well as modeling and simulation software. Four medical centers and more than 60 patients have been involved in a one-year randomized controlled evaluation, which showed promising clinical and evaluation results, although not statistically significant in all medical centers [11].

R. Bellazzi

C. Supporting health care policy makers Policy makers are now dealing with the problem of DM risk stratification, in order to plan for tailored life-style and disease management interventions. A large number of studies are available on the definition of DM risk calculators, based on simple data which can be collected in primary care visits, such as Age, BMI, waist circumference, antihypertensive drug treatment; those calculators are often coupled with cardiovascular risk calculators, to compute the overall health risk profile [26]. Furthermore, the investigation of insulin resistance has received considerable attention over the last twenty years, leading to quantitative indexes like the quantitative insulin-sensitivity check index (QUICKI) [27] and the so-called “minimal model” [28,29]. Moreover, in the last few years, research is actively working to find novel markers of Diabetes risk based on genetic patient profiling. Current studies are looking for genes associated with phenotypic traits of the metabolic syndrome [30]. Less attention have been devoted to the exploitation of the information coming from the patients’ administrative data [31] collected by health care system, such as hospital admissions and discharges, drug prescriptions, ambulatory visits. This challenge seems particularly relevant for policy markers who seldom rely on health care information systems integrated with clinical EPR for DM management. The exploitation of Data Mining techniques, and in particular of temporal Data mining tools, may lead to define temporal patterns associated with the disease diagnosis. Such patterns may highlight precedence relationships between physicians prescriptions and patients outcomes which may lead to properly stratify the available population [32]. Finally, health care decision makers have been recently provided with new tools for decision analysis based on the so-called DM modeling, i.e. mathematical models of the disease progression which can be used to simulate the effect of novel strategies on a large population of patients, and to take decisions formally evaluating projected costs and benefits of such strategies [33]. Up to now, there are no integrated tools for risk stratification and decision making able to integrate the available data sources, the current research results and the more recent modeling efforts in a comprehensive solution for identifying subjects who needs tailored interventions. Research and technical implementations are needed to improve the implementation of prevention programs. III. CONCLUSIONS The need of new kind of interventions for chronic care management is related to two concurrent factors, the increase of the number of elderly chronic patients and the

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Information Technology Solutions for Diabetes management and prevention Current Challenges and Future Research directions

difficulty to improve their clinical outcomes. The availability of integrated health care information system is enabling the implementation of novel disease management and prevention programs, heavily relying on communication between citizens and health care providers, Current research in ICT may provide suitable tools and instruments to increase the quality of such programs by empowering patients and by optimizing the required organizational efforts.

ACKNOWLEDGMENTS I acknowledge Dr. Timothy Bickmore and Toni Giorgino for their help in writing this review.

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3. 4.

5. 6. 7.

8.

9. 10. 11.

12.

13.

Wild s, Roglic G, Green a, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes care. 2004;27(5):1047-53 Kilpatrick ES, Rigby AS, Atkin SL. Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: "double diabetes" in the Diabetes Control and Complications Trial. Diabetes Care. 2007; 30(3):707-12. UKPDS 33, Lancet 1998;352:837-53 Herman W, et al. The Cost-Effectiveness of Lifestyle Modification or Metformin in Preventing Type 2 Diabetes in Adults with Impaired Glucose Tolerance, Annals of Internal Medicine. 2005; 142 (5): 323332. Glasgow RE, et al. Report of the health care delivery work group: behavioral research related to the establishment of a chronic disease model for diabetes care. Diabetes Care. 2001; 24(1):124-30. Cavan DA, et al. Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes. Comput Methods Programs Biomed. 1998; 56(2):157-64. Hetlevik I, Holmen J, Kruger O, Kristensen P, Iversen H, Furuseth K. Implementing clinical guidelines in the treatment of diabetes mellitus in general practice. Int J Technol Assess Health Care. 2000; 16(1):210-27. Montani S, Bellazzi R, Portinale L, d'Annunzio G, Fiocchi S, Stefanelli M. Diabetic Patients Management Exploiting Case-Based Reasoning Techniques, Comput. Meth. and Progs. in Biomedicine 2000; 62: 205-218. Gomez EJ, Del Pozo F, Hernando E. Telemedicine for Diabetes care: the DIABTel approach. Medical Informatics, 1996; 21:283-295 Starren J, et al. Columbia University's Informatics for Diabetes Education and Telemedicine (IDEATel) project: technical implementation. J. Am Med Inform Assoc. 2002; 9(1):25-36. Larizza C, Bellazzi R, Stefanelli M, Ferrari P, De Cata P, Gazzaruso C, Fratino P, D'Annunzio G, Hernando E, Gomez EJ. The M2DM Project--the experience of two Italian clinical sites with clinical evaluation of a multi-access service for the management of diabetes mellitus patients. Methods Inf Med. 2006; 45(1):79-84. Sperl-Hillen J, O'Connor PJ, Carlson RR, Lawson TB, Halstenson C, Crowson T, Wuorenma J. Improving diabetes care in a large health care system: an enhanced primary care approach. Jt Comm J Qual Improv. 2000; 26(11):615-22. Prochaska, J., & Marcus, B. (1994). The Transtheoretical Model: Applications to Exercise. In R. Dishman (Ed.), Advances in Exercise Adherence (pp. 161-180).

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14. Glanz, K., Lewis, F., & Rimer, B. (1997). Health Behavior and Health Education: Theory, Research, and Practice. SF, CA: Jossey-Bass. 15. Owen, N., Fotheringham, M., & Marcus, B. (2002). Communication technology and health behavior change. In K. Glanz, B. Rimer, F. Lewis (Eds.), Health behavior and health education. SF, CA: JosseyBass. 16. Ma C, Warren J, Phillips P, Stanek J. Empowering patients with essential information and communication support in the context of diabetes. Int J Med Inform. 2006. 75(8):577-96. 17. Cassell, J., Sullivan, J., Prevost, S., & Churchill, E. (2000). Embodied Conversational Agents. Cambridge: MIT Press. 18. Bickmore, T. and Picard, R. (to appear) "Establishing and Maintaining Long-Term Human-Computer Relationships" ACM Transactions on Computer Human Interaction (ToCHI). 19. Koch S. Meeting the challenges--the role of medical informatics in an ageing society. Stud Health Technol Inform. 2006;124:25-31. 20. Lehmann ED, Deutsch T. Application of computers in Diabetes care – a review. Part I and II. Med Inform. 1995;20(4):281-329. 21. Lehmann ED, Deutsch T. Computer assisted diabetes care: a 6-year retrospective. Comput Methods Programs Biomed. 1996, 50(3):20930. 22. Dorr D, Bonner LM, Cohen AN, Shoai RS, Perrin R, Chaney E, Young AS. Informatics Systems to Promote Improved Care for Chronic Illness: A Literature Review. J Am Med Inform Assoc. 2007 14(2):156-163. 23. O'Connor PJ, Crain AL, Rush WA, Sperl-Hillen JM, Gutenkauf JJ, Duncan JE. Impact of an electronic medical record on diabetes quality of care. Ann Fam Med. 2005 3(4):300-6. 24. Leese G., Boyle D., Morris A. The Taysisde Diabetes Network. Diabetes Research and Clinical Practice, 2006, 74,S197-S199. 25. Shea S, et al, A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus. J Am Med Inform Assoc. 2006;1340-51. 26. Lindstrom J, Tuomilehto J, The Diabetes Risk Score. A practical tool to predict type 2 diabetes risk Diabetes Care 26: 725-731,2003. 27. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ: Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85: 2402-2410, 2000 28. Magni P, Sparacino G, Bellazzi R, Toffolo GM, Cobelli C. Insulin minimal model indexes and secretion: proper handling of uncertainty by a Bayesian approach. Ann Biomed Eng. 2004 32(7):1027-37. 29. Bergman R, Ider Y, Bowden C, Cobelli C: Quantitative estimation of insulin sensitivity. Am J Physiol 236: E667-E677, 1979 30. Sladek R, et al., . A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445(7130):881-5. 31. Kristensen JK, Sandbaek A, Lassen JF, Bro F, Lauritzen T. Use and validation of public data files for identification of the diabetic population in a Danish county. Dan Med Bull. 2001 Feb;48(1):33-7. 32. Bellazzi R, Larizza C, Magni P, Bellazzi R. Temporal data mining for the quality assessment of hemodialysis services. Artif Intell Med. 2005;34(1):25-39. 33. Herman W., Diabetes Modeling. Diabetes care, 2003; 26 (11): 3182 Author: Ricccardo Bellazzi Institute: Dipartimento di Informatica e Sistemistica, Universita di Pavia Street: via Ferrata 1 City: Pavia Country: Italy Email: [email protected]

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Patient-Cooperative Rehabilitation Robotics in Zurich Robert Riener1,2 1

Sensory-Motor Systems Laboratory, ETH Zurich, Switzerland Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland

2

Abstract— This paper gives a short overview of new patientcooperative robotic approaches applied to the rehabilitation of gait and upper-extremity functions in patients with movement disorders. So-called patient-cooperative controllers take into account the patient’s intention and efforts rather than imposing any predefined movement. Audiovisual displays in combination with the robotic device can be used to present a virtual environment and let the patient perform different gait tasks and activities of daily living. Furthermore, the sensors implemented in the robots allow to measure and assess the patient performance and, thus, evaluate the therapy status. It is hypothesized that such patient-cooperative robotic approaches can improve patient motivation and the quality of the therapy compared to conventional approaches. Keywords— Rehabilitation Robotics, Gait Therapy, Treadmill Training, Arm Therapy, Patient-Cooperative

I. ROBOT-AIDED REHABILITATION Task-oriented repetitive movements can improve muscular strength and movement coordination in patients with impairments due to neurological or orthopaedic problems. A typical repetitive movement is the human gait. Treadmill training has been shown to improve gait and lower limb motor function in patients with locomotor disorders. Similarly, repetitive arm therapy is used for patients with paralysed upper extremities after stroke or SCI. Several studies prove that arm therapy has positive effects on the rehabilitation progress of stroke patients. It was observed that more and longer training sessions per week and longer total training periods have a positive effect on the motor function. The finding that the rehabilitation progress depends on the training intensity motivates the application of robot-aided therapy [1-5]. In contrast to manually assisted movement training, with automated, i.e. robot-assisted, gait and arm training the duration and number of training sessions can be increased, while reducing the efforts spent by the therapists per patient. Furthermore, the robot provides quantitative measures, thus, allowing the observation and evaluation of the rehabilitation process [1]. An example for a typical arm therapy robot is presented in Fig. 1.

II. PATIENT-RESPONSIVE CONTROL So-called “patient-responsive” strategies will recognize the patient’s movement intention and motor abilities in terms of muscular efforts and adapt the robotic assistance to the patient’s contribution. The best control strategy will do the same as a qualified human therapist – it will assist the patient’s movement only as much as needed. This will allow the patient to actively learn the spatiotemporal patterns of muscle activation associated with normal gait and arm/hand function. The term “patient-responsive” comprises the meanings of compliant, because the robot behaves soft and gentle and reacts to the patient’s muscular effort, adaptive, because the robot adapts to the patient’s motor abilities, and supportive, because the robot helps the patient and does not impose a predefined movement. It is assumed that patient-responsive strategies will maximize the therapeutic outcome. Among many different patient-responsive controllers developed so far [2] one of the most promising is a “pathcontroller”, where not the robot, but the patient is controlling the timing of the movements. The patient can move freely along a path corresponding to a physiological walking pattern, and is corrected by a surrounding force field when he/she deviates from the path. An additional

Fig. 1 The Zurich arm rehabilitation robot ARMin [3-5]

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Robert Riener

multi-modal (visual, acoustic, and haptic) displays allowing the patient to solve any virtual activity of daily living (ADL). These VR tasks can increase the motivation of the patients to participate. IV. ROBOT-AIDED PATIENT ASSESSMENT

Fig. 2 Lokomat equipped with a multimodal display comprised by a stereo projection system, a doubly surround sound system, a wind-generating fan, and the Lokomat providing haptic feedback. Virtual objects can be projected motivating the patient to lift the leg.

supportive force can assist the patient’s efforts as much as needed. The strategy can be adapted to the individual patient’s capabilities. III. BIOFEEDBACK AND VIRTUAL REALITY Optimal training effects during gait therapy depend on appropriate feedback about performance. For the patient, the quality of movement and extent of activity are significant measures of performance that are not easily assessed subjectively, particularly when there are also deficits in sensation, proprioception, and cognition. The robotic devices ARMin and Lokomat are instrumented with potentiometers and force transducers, and thus, are capable of providing online feedback about joint movement and joint moment production, respectively. The feedback values enable easy presentation by graphical, acoustical, or tactile displays to the patient motivating him/her to improve his/her gait pattern during the therapy. Special Virtual Reality (VR) techniques are being established allowing the patients to perform specific gait or reach-and-grasp tasks. For example with the Lokomat virtual obstacles can be displayed that must be crossed by the patient (Fig. 2). An acoustic display generates the step sound and other environmental sound sources. Hitting the obstacle can be seen, heard and felt by a 3D screen, surround sound system and a force displayed by the Lokomat, respectively. A fan produces a wind that increases its intensity with increasing gait speed. Similarly, with the ARMin system any virtual scenario can be generated by

Compared to manual treadmill therapy, there is a loss of physical interaction between therapist and patient with robotic gait retraining. Thus, it is difficult for the therapist to assess the patient’s contribution and to provide necessary feedback and instructions. The values recorded by the robot sensors can provide feedback not only to the patient but also to the therapist allowing him to evaluate the patient’s effort and assess the therapeutic progress. Important measures to be assessed are primary and secondary impairments originating from brain or spinal cord injuries including muscle weakness/strength, muscle tone/spasticity, active and passive joint range of motion etc. These measures provide important outcome indicators for the therapist in assessing functional improvement with any therapy. Performing each of these tests during every rehabilitation session would be time-consuming. However, implementation of tests that can measure these parameters could be achieved by appropriate instrumentation of robotic devices. The enhancement of the robotic trainers would be a viable approach because no additional acquisitions are required. For example, force transducers offer a means to evaluate muscle strength and voluntary force. Potentiometers offer a convenient method to extract joint range of motion information. Last but not least, imposing joint movements at different speeds and concomitant measurements from force transducers offer a possibility to evaluate passive joint stiffness as well as active and passive muscle properties. V. CONCLUSION The aim of patient-responsive control strategies is to consider voluntary efforts and exploit remaining natural control capabilities of the central nervous system after damage of brain or spinal cord. The force information is used to adapt the robotic assistance to the patient’s motor abilities enabling the patient to contribute as much as possible to the movement. At the same time, force and movement recordings can be displayed to the patient for biofeedback purposes and serve the therapist to evaluate the long term results of the movement therapy. The effects of the responsive strategies on the patient can be compared to the behavior of a qualified human therapist,

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Patient-Cooperative Rehabilitation Robotics in Zurich

who moves the patient’s limbs with some amount of compliance. It is expected that the above-mentioned patientcooperative strategies will stimulate active participation by the patient and maximize the therapeutic outcome in terms of reduced therapy duration and an improved gait quality. The high potential for future robot-aided treadmill training lies in the combination of robot-assisted training with robot-assisted assessment. Thus, only one device is required to do both training and assessment. No additional efforts of donning and doffing are necessary, because the patient can use the training device also for the assessment before, during or after the therapy. Furthermore, the instrumented robotic actuation makes training as well as assessment not only repeatable, but also recordable. This is an important prerequisite for intra- and inter-subject comparisons required to assist the therapist in the evaluation of the rehabilitation process. In summary, patient-cooperative rehabilitation robotics has a high potential to make future gait and arm therapy easier, more comfortable, and more efficient. However, broad clinical testing is still required to prove these assumptions.

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Union (Marie-Curie Project “MIMARS”) Bangerter-Rhyner-Foundation, Switzerland.

and

the

REFERENCES [1] Riener, R., Lünenburger, L., Colombo, G. (2006) Humancentered robotics applied to gait training and assessment, Journal of Rehabilitation Research & Development 43, pp. 679-694. [2] Riener, R., Lünenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V. (2005) Cooperative subject-centered strategies for robot-aided treadmill training: first experimental results. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13, p. 380-393. [3] Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43, p. 2-10. [4] Mihelj, M., Nef, T., Riener, R. (2006) A novel paradigm for patient cooperative control of upper limb rehabilitation robots, Advanced Robotics, in press. [5] Nef, T., Mihelj, M., Colombo. G., Riener, R. (2006) ARMin – Robot for rehabilitation of the upper extremities. IEEE Int. Conference on Robotics and Automation, ICRA 2006, Orlando. p. 3152-3157.

ACKNOWLEDGEMENTS I thank my team of the Sensory-Motor Systems Lab, ETH and University Zurich, and the team of the SCI Center, Balgrist University Hospital for their contributions to this work. Special thanks go to Dr. Matjaz Mihelj who contributed major parts to the ARMin developments. This project was partially supported by the Swiss National Science Foundation NCCR Neuro (project 8), the European

Author: Institute: Street: City: Country: Email:

Robert Riener ETH Zürich SMS-Laboratory Tannenstrasse 1 TAN E 4 8092 Zürich Switzerland [email protected]

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Systemic Electroporation – Combining Electric Pulses with Bioactive Agents Eberhard Neumann Physical and Biophysical Chemistry, Faculty of Chemistry, University of Bielefeld, Bielefeld, Germany Abstract— In the year 2007, the documentation of the first functionally effective electrotransfer of naked DNA by electroporation, with stable gene expression, is 25 years old. This first functional electro-uptake has been preceded, in 1972, by the first documentation of controlled electrorelease of cellular components from bovine medullar chromaffin granules. - In the meantime, the electroporation field pulse techniques combined with the application of bioactive agents, have culminated in the new clinical disciplines of electrochemotherapy and electrogenetherapy. There are continuously ongoing efforts to improve the pulse protocols by optimizing equipment and cell biological strategies, relying heavily on the increasing knowledge on molecular-mechanistic details derived from the various electroporation data. - The digression here is restricted to a survey-like appreciation of the early functional electroporation data and their thermodynamic and mechanistic interpretation. We briefly touch the potential to explore systemic electroporation for the treatments of tissue, in particular, tumors. The goal is to provide tools in order to optimize systemic electroporation protocols and the design of electrode arrays for clinical use. Keywords— DNA electrotransfer, electrorelease, dipole rearrangements, systemic electroporation

I. INTRODUCTION The Medicon 2007 happens to be also the frame of the 25th anniversary of the first documentation of “The electric pulse method for the stable transformation of biological cells with naked gene DNA“, celebrating the first functionally effective electro-transfer of nonviral DNA by electroporation with stable gene expression in 1982 [1,2]. Complementary to electro-uptake, ten years earlier, in 1972, the electric pulse technique had been used to achieve the first non-destructive electro-release of cellular components like catecholamines, ATP and chromogranine proteins from isolated chromaffin granules of bovine adrenal medullae [3]. These initial physical chemical studies on the fieldcontrolled electroporative uptake and release of mole- cules have been recently valued in Nature Methods [4] as seminal for the various biotechnological and medical applications of what now may be called „Systemic Electroporation“, i.e., the application of voltage pulses combined with bioactive agents.

The recent developments of the techniques of systemic electroporation culminate in the new clinical disciplines of electro-chemotherapy and gene electrotherapy (L. Mir, R. Heller, D. Miklavcic, J. Teissie, G. Sersa, et al.). The following digression is restricted to a critical appreciation of the early data of functional electro-poration and the early molecular-mechanistic proposals for pore formation in high electric fields, along some of the original references (E. Neumann et al.). II. PORATION THERMODYNAMICS A. Electrochemical ansatz Already in 1982, a simple chemical scheme for the structural overall transition from a cascade of field sensitive closed membrane states (C) to the sequence of porous states (P) has been chemically formulated as: (C)

(P)

(1)

The overall distribution equilibrium constant of this overall scheme is specified as the state density ratio K= (P)/(C) = f/(1-f) and the fraction of pore states is f = (P)/[(P)+(C)]. It is found that both, K and f, increase with increasing field, maximally up to a few 0.1 percent in cells, and a few percent in curved lipid bilayers. A general analytical ansatz has been specified in terms of a generalized van t’ Hoff relationship, covering as a total differential, changes dT in temperature T in Kelvin units, changes dp in pressure p and changes dE in the strength E of the locally active electric field, relative to the molar energy unit RT, RT ⋅ d n K = (ΔH° / T) p,E dT − (ΔV° )T,E dp + ( ΔM°) p,T dE

(2)

Here R = k N A is the gas constant, k the Boltzmann constant and N A the Loschmidt-Avogadro constant. Note that Eq.(2) refers to three physical poration phenomena. Electroporation is characterized by the standard value ΔM 0 = M(P) - M(C) of the reaction dipole moment for the C to P transition, sono-poration by the standard reaction volume ΔV° and thermo-poration, including laser optoporation, by the standard reaction enthalpy ΔH° . Note that ΔH° is the total energy at constant pressure p, at a given

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Systemic Electroporation – Combining Electric Pulses with Bioactive Agents

temperature T and field strength E. Note that, for field effects, we have to specify the reaction enthalpy as ˆ + TΔS ΔH = ΔG

(3)

where the work potential ΔGˆ = ΔG − E ⋅ M is the Legendre-transformed Gibbs reaction energy, ΔG the ordinary reaction Gibbs energy in the field E, M the projection of the total electric moment vector M onto the direction of E, and ΔS the reaction entropy, all at constant outside pressure. It is worth mentioning that Eq. (3) reflects the first law of electro-thermodynamics at constant pressure in a canonical ensemble, i.e., a reactive system neighboured by other molecules. The electro-thermodynamic standard term ΔGˆ ° is the reversible extra work and T Δ S° refers to the reversible, i.e., exchangeable, heat energy. B. Chemical Electrothermodynamics The connection between the experimental signal (S) and the fractional effect f=S/ Smax and the electrothermodynamic quantities is via the relationships K = f/(1-f). The field dependence of K (and thus of the fraction f) is given by: K(E) = K(0) ⋅ exp X(E)

(4)

Eq. (4) reflects the electrochemical equilibrium condition ˆ = 0 , such that for dipolar molecular organizations ΔG ˆ ° results. The field effect factor is specified RT ⋅ n K = −ΔG as X(E loc ) =

∫ ΔM ⋅ dEloc RT

,

(5)

where E loc is the local field. If we refer to permanent dipoles, E loc is the directing field E dir , calculated from the induced field in given by: E m = - Δϕm /d m ,

(6)

where d m is the membrane of thickness d m , Δϕm the potential difference across the membrane. According to Maxwell, the electric current density vector for cross membrane cation and anion flows is given by. jm = σm (−∇ϕm ) = σm E m

(7)

where σ m is the membrane conductivity (of all the pores), The stationary value of the (Maxwell-Wagner) polarization-induced electric potential difference Δϕm (or Einduced membrane potential) for spherical membranes with the scalar value a of the radius vector r, under the angle θ to the direction of the homogeneous external field vector E, is given by:

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Δϕm (θ) = - (3/2) ⋅ a ⋅ E ⋅ f σ cosθ

(8)

applicable for the description of current flows, consistent with Eq. (7), through the two hemispheres of a spherical membrane shell. Integration within the boundaries θ = 0;π yields the average membrane potential: Δϕm = −

3 E⋅a ≈ − E⋅a , 2

(9)

as a practically useful approximation for isolated cells as well as for densely packed cells in cell pellets or tissue. The average membrane field amplification is then given by: Em ≈ E a / dm

(10)

For instance, the amplification factor of a sphere with radius a = 10 µm and d m = 10 nm is a/d m = 10−3 . Finally, substitutions into Eqs. (4) and (5) result in expressions which permit to determine the reaction dipole moment or the polarization volume (giving the pore radius). Substitution of the respective expressions for K = f /(1-f) yields the relationship for the field-induced fractional change, Δ f = f(E) - f(0), relative to the zero-field fluctuation term f(0) as: ⎛ K 0 exp X K0 Δf = ⎜ − 1 + K exp X 1 + K0 0 ⎝

⎞ K 0 (exp X − 1) ⎟= ⎠ (1 + K 0 exp X)

(11)

In the case that, at E = 0, the condition K 0 1 holds true, the second part of Eq. (11) is a very good approximation of the first part. Eq. (11) has been frequently used to determine reaction dipole moments and/or reaction volumes and thus average pore radii, independent of the selective assumption of a balance of electric free energy and line/surface tension energies. It is also important to note that the exponential time courses indicate that it is the number of defined pores of a given type, which increases with increasing field strength, and that the data do not indicate continuous pore expansion. C.Reaction couplings and normal modes

In practical work, it has turned out that the overall state (C) in scheme (1) represents a whole cascade of several steps ( C1 C2 ... ) and that also (P) must be replaced with ( P1 P2 ... ). The electro-thermodynamic analysis is then more complex, too. For instance, if the kinetic data are consistent with the state transition cascade C P1 P2 , the respective terms are: K1 = P1 / C and f1 = P1 /(P1 + C) , and K 2 = P2 / P1 and f 2 = P2 /(P1 + P2 ) , both steps associated with the re-

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Eberhard Neumann

spective separate reaction moments and polarization volumes. The single normal modes are characterized by the fielddependent normal mode relaxation times and amplitudes, yielding molecular polarization volume of each mode, and thus the respective pore radii and fractions of pores. The data on cells, isolated or in densely packed cell pellets, and on lipid vesicles, are consistent with at least two types of pores. The first type is a kind of perm-selective “NernstPlanck” pore, permitting transport either of cations or of anions, which thus go separately through different pores. So, on average, half of the pores transport cations and, parallel to it, the other half counter-transport anions. The transport of this type is a kind of ion exchange, e.g., cations go in on one hemisphere and go out on the other hemisphere such that there is no net transport of ions, for instance, out of the compartment. The data further suggest that these ion exchange pores (of radius r/nm =0.8 ± 0.2) can, at a higher pore density, develop larger pores at the expense of the smaller ones. The lager pores (r ≥ 1 nm) permit net transport of ions, for instance, net outflow from a cell of higher internal ion concentration than the outside. Further progress in the theory shows that the observation of field strength thresholds for measurable signals, as quantified in strength-duration curves, can be rationalized as a limit in the experimental detection (“visibility” threshold). The experimental threshold does certainly not just reflect the energetic balance of electric polarization and surface tension and (counter-acting) line tension. III. ELECTROPORATIVE TRANSPORT In essence, the field primarily acts on the structure, leading to pores of defined size. Concomitant transport is thus structurally controlled, the permeability changes both are based on the poration steps and resealing processes. Therefore the kinetics of electroporative transport also reflects the underlying structural changes, i.e., number and type of transport passages. Proper analysis provides, on the one hand permeability coefficients for the diffusion of molecules across concentration gradients. On the other hand, we obtain the kinetic parameters of the field-dependent pore formation and resealing processes. It is recalled that the mole flow (mol/s) of ion transport must be formulated in terms of the, recently introduced, concept of time-dependent transport coefficients. These coefficients explicitly reflect the, usually exponential, kinetics of poration processes and the flow coefficient of resealing indicates the much slower process of pore resealing. In

any case, the measured transport curves, like the time courses of conductance changes are therefore exponentials of exponentials. This can deceive smeared exponentials of the “Kohlrausch-type”. Specifically, the time courses reflect, in a folded form, the change in the fraction of pores, because the mole flow is proportional to the flow area, i.e., to the increasing or decreasing number of pores. Therefore, proper analysis starts with the mole flow, and not with the mole flow density (mol/s m²) or mole flux (=flow per area), in order to rationalize time-dependent flow coefficients. In addition, the value of the flow coefficient at the time point of the end of the applied pulse, yields the kinetic parameters for the rate limiting, (primary) structural transitions, preceding the (secondary) transport processes. IV. LOCAL LIPID REARRANGEMENTS

(PORES)

Molecular mechanistic, it had been presumed and explicitly indicated already in 1982 that the field forces cause lipid rearrangements such that the locally the charged groups and dipolar lipid head groups form a specific pore wall like that in hydrophilic or inverted pores. The dipolar head groups were drawn as aligned parallel to the external field direction [1]. This presumption has been recently supported by both, relaxation kinetic data obtained with small lipid bilayer vesicles, and by molecular dynamics simulations of the molecular rearrangements of lipid and water molecules involved in membrane electroporation. The technique of cell electroporation has been recently extended to ultra-short pulses with nominally very high external electric field strengths. At these high external field strengths and the short rise times, probably the rapid dielectric polarization of interfaces, besides the slower MaxwellWagner ionic polarization, may rapidly induce locally larger fields which, in turn, affect primarily the intracellular organelles. Ultra-electroporation is supposed to have powerful clinical potential, for instance, for inducing cell apoptosis in malignant tissue (K. Schoenbach, J.C. Weaver), Analytically, if the experimental electroporation times are expressed as a function of the respective directing field, (calculated from the external field using the dielectric constant of the polar environment), the entire field strength range, from moderate field strengths and short pulse times up to the very high external fields of the ultra-short pulses, can be consistently described with one and the same permanent dipole moment. The electro-thermodynamic analysis yields the mean dipole moment of 91.7 ( ± 5.0) 10-30Cm (27.4 ± 1.4 D) associated with the elementary unit, involved in a defined dipolar rotation process.

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Systemic Electroporation – Combining Electric Pulses with Bioactive Agents

If we compare this value with the dipole moment of the zwitterionic phosphatidylcholine head group of (70 ± 5)10-30 Cm (21 ± 2 D), we may conclude that the hydrated ionic and dipolar lipid head groups, where the water molecules in the asymmetric hydration shells of the ionic groups lead to higher dipole moments, are the molecular receptors for the interaction of the local field with the membranes. Further on, we quantify rotations of these dipolar field receptors into field-parallel positions in the walls of the hydrophilic (inverted) electropores, as one type of the dominant elementary processes in membrane electroporation. Besides the structural (C) = (P) model and the suggestion of direct field effects on the dipolar lipid head groups, the membrane electroporation processes have been viewed as a structural hysteresis with rapid in-field electric pore formation and much slower field-off resealing [5]. Since the late seventies pure physical theories have been developed and they continue to provide additional deeper insights (K. Kinosita, Jr., T.Y. Tsong, Y.A. Chiz-madzhev, J.C.Weaver) into physical details of. On the level of densely packed cells and tissue, conductivity data have been interpreted as models for tissue. For instance, field distributions across tissue between electrodes have been calculated (D. Miklavcic) and successfully applied for the design of new electrode arrays and medical electroporators. V. CONCLUSIONS In summary, the first functional electrotransfer of DNA in 1982, was the starting point of many fruitful studies, also aiming at the optimization of the guidelines of cell biological and clinical electroporation protocols and for the devel-

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opment of new electrode arrays and electro-porators such as the semi-automatic EU- Cliniporator©.

ACKNOWLEDGMENT We gratefully acknowledge financial support by the DFG, Bonn, grants Ne-227/1-9; by the ministry MSWF of the Land NRW for grant ELMINOS; by the European Union, Brussels, center grant QLK3-CT-1999-00484.

REFERENCES 1. 2. 3. 4. 5.

Neumann E, Schaefer-Ridder M, Wang Y, Hofschneider PH (1982) Gene transfer into mouse lyoma cells by electro-poraton in high electric fields, EMBO J, 1, 841-845 Wong TK, Neumann E (1982) Electric field mediated gene transfer, Biophys Biochem Res Commun 107, 584-587 Neumann E, Rosenheck K (1972) Permeability changes induced by electric impulses in vesicular membranes, J Membr Biol 10, 279-290 and (1973) 14, 194-196 Eisenstein M (2006) A look back: a shock to the system, Nature Methods, 3, 66 Neumann E (1989) The relaxation hysteresis of membrane electroporation, In: Electroporation and electrofusion in cell biology. Eds. Neumann E, Sowers AE, and Jordan CA, Plenum Press, p.61-82 Author: Eberhard Neumann Institute: Physical and Biophysical Chemistry, Faculty of chemistry, University of Bielefeld Street: P.O. Box 100 131 City: Bielefeld Country: Germany Email: [email protected]

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An algorithm for classification of ambulatory ECG leads according to type of transient ischemic episodes A. Smrdel1 and F. Jager1 1

Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

Abstract— We developed and evaluated an algorithm for classification of ECG leads in ambulatory records according to type of transient ischemic ST segment episodes using the LongTerm ST Database. The algorithm robustly generates ST level function in each ECG lead, tracks non-ischemic ST changes to construct the ST reference function and subtracts it from the ST level function to obtain the ST deviation function. Then the algorithm using statistical moment of the histogram of the ST deviation function given lead classifies the lead according to type of transient ischemic ST episodes (elevations, depressions). The algorithm correctly classified all 9 ECG leads with elevated and 89 out of 90 ECG leads with depressed transient ischemic ST episodes.

severe cases of ischemic heart disease, such as Prinzmetal's angina, while the depressed ischemic episodes appear in patients with milder cases of ischemic heart disease, such as stable angina. Furthermore, elevated ST segment indicates high risk of mortality [2], an ischemic injury [3], or an acute myocardial infarction [4]. As a diagnostic tool it would be beneficial to be able to determine the severeness of the heart disease by analyzing ambulatory ECG records and determining orientation of leads. In this paper we present an algorithm to classify ambulatory ECG leads according to their orientation. The algorithm was developed and evaluated using the LTST DB.

Keywords— Ambulatory electrocardiogram, transient ischemia, ECG lead classification, Long-Term ST Database.

I. INTRODUCTION Ischemia is one of the most common heart diseases. It is a state when there is insufficient supply of heart muscle with oxygenated blood. This can lead to myocardial infarction and, in worst case, death. In electrocardiogram (ECG), ischemia is manifested as change of ST segment morphology (ischemic ST segment changes). In addition, nonischemic changes of ST segment morphology are similar to true ischemic changes and include: heart-rate related ST segment changes, changes of ST segment level due to sudden shifts of the electrical axis of the heart (axis shifts) due to body position changes, and slow drifts of ST segment level due to diurnal changes or effects of medications. Ischemic changes and non-ischemic heart-rate related changes construct transient ischemic and transient heart-rate related ST segment episodes respectively. According to the shift of the ST segment level (positive or negative), the ST segment episodes are elevated or depressed. If we observe ambulatory ECG records, we notice, that majority of ECG leads contain only one type of episodes: elevations or depressions, while only a small number of leads contain both types. Therefore we can define the lead orientation such as: positive (only elevations present), negative (only depressions), mixed (elevations and depressions), and no orientation (no episodes). Elevated ischemic ST episodes, as we can observe in ambulatory ECG records of the Long-Term ST Database (LTST DB) [1], usually appear in patients with

II. MATERIAL AND METHODS The LTST DB includes 86 2- and 3-lead 24-hour ambulatory records (190 ECG leads), obtained during daily clinical practice. Each record contains reference annotations (set by human expert annotators) for ischemic and heart-rate related ST segment episodes, and human expert reference annotations that define time-varying ST segment reference level (non-ischemic path). By subtracting time-varying ST segment reference level from actual ST segment level the ST segment deviation level or the ST segment deviation function is obtained in which transient ischemic and heartrate related ST segment episodes are annotated. These ST segment episodes were annotated according to annotation criteria, which state that: • • •

an episode begins when the magnitude of the ST deviation function first exceeds 50 μV; the ST deviation function must reach a magnitude of Vmin or more throughout the interval of at least Tmin s; the episode ends when the ST deviation function becomes smaller than 50 μV, provided that it does not exceed 50 μV in the following 30 s.

Values for Vmin and Tmin differ according to three annotation protocols: • • •

Protocol A: Vmin = 75 μV; Tmin = 30 s; Protocol B: Vmin = 100 μV; Tmin = 30 s; Protocol C: Vmin = 100 μV; Tmin = 60 s; For this study we chose reference annotations according to protocol B. For each lead of each record of the LTST DB, we

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An algorithm for classification of ambulatory ECG leads according to type of transient ischemic episodes

visually verified the episodes in order to determine the lead orientation: if the extrema of all episodes in the lead were positive, the lead has positive orientation; if the extrema of all episodes were negative, the lead has negative orientation; if the episodes had negative and positive extrema the lead orientation is mixed; and if there were no episodes, the lead has no orientation. In our study we included leads with transient ischemic ST episodes only (104 leads), and leads without any ischemic or heart-rate related ST episodes (42 leads). Of the former, 9 have elevated episodes only, 90 have depressed episodes only, while 5 leads have elevated and depressed episodes. Leads which contain heart-rate related episodes or both types were excluded (44 leads). The algorithm developed for classification of leads according to their orientation is based on our previous work [5,6]. It is composed of following modules. A. Preprocessing The algorithm initially robustly preprocesses raw ECG data, and extracts noises and abnormal heart beats. To further avoid the effects of noise the average heart beats are constructed. Each normal heart beat in the raw ECG signal is replaced with the average heart beat. For the construction of each average heart beat that replaces individual current heart beat normal non-noisy heart beats in the 16 s neighborhood of the current heart beat are used. To construct the ST level function given lead, the algorithm first searches for the isoelectric levels and J points in each average heart beat. To determine the isoelectric level, I(j), where j denotes average heart beat number, the algorithm searches for the “flattest” part of the signal from the fiducial point, FP(j), backwards [5]. For the position of the J point, J(j), the algorithm searches forward from the FP(j) for a part of a waveform that “starts to flatten” [5]. After that, the ST segment amplitude is measured in each average heart beat and in each lead as the difference between the amplitudes at the point of measurements of ST segment level, J(j)+ 80 ms, and isoelectric level, I(j). These measurements constitute `fine' ST level function, s(i,j), where i denotes the lead number. This function is then resampled (ΔT=2 s) and smoothed to obtain the `raw' ST level function, s(i,k), where k denotes the sample number. Examples of the ST level function derived by the human expert annotators of the LTST DB and that derived by the algorithm are shown in Fig. 1.b and 1.d respectively. B. Tracking of slow drifts and detection of axis shifts Non-ischemic events have to be excluded from the ST level function. The algorithm tracks the non-ischemic path in the ST level function to create the ST reference function,

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which is then subtracted from the ST level function to obtain the ST deviation function. In the first step, slowly varying reference trend is tracked. The filters of 6 h 40 min ( h g ) and 5 min ( hl ) in duration are applied to obtain the timevarying global, rg (i, k ) , and local, rl (i, k ) , ST reference level trends respectively. Then the estimation of the reference level, r1 (i, k ) , which tracks slow drifts, but skips faster events and episodes is obtained using: ⎧⎪rg (i, k ) : if rg (i, k ) − rl (i, k ) > 50 μV r1 (i, k ) = ⎨ ⎪⎩rl (i, k ) : otherwise.

(1)

In the second step, axis shifts due to body position changes are detected. To detect step changes of the ST level function the ST level function and the Mahalanobis distance functions of the first order of the QRS complex and ST segment Karhunen-Loève (KL) coefficient feature vectors are used. Axis shifts are detected in all three functions as a step change which has a flat interval before and after the step change. In each of the three functions, the algorithm first searches for a flat interval of T f =216 s in length, which has to have its mean absolute deviation from its own mean less than K f =20μV for the ST level function, and less than Σ F =0.33 SD (SD - standard deviation) for both Mahalanobis distance functions of the first order. This has to be followed by a step change, characterized by the moving average value over Ta =72 s in length, and has to change for at least K S =50 μV for the ST level function and for at least Θ QRS =0.5 SD for the QRS KL distance function and for at least Θ ST =0.4 SD for the ST KL distance function in the next 2Ta =144 s in length. This step change has to be followed by another flat interval in all three functions defined the same as for the first flat interval. In the intervals surrounding the step change, the ST reference function is updated, following: ⎧r1 (i, k ) : if rg (i, k ) − s (i, k ) < 50μV ∧ ⎪⎪ r2 (i, k ) = ⎨ rl (i, k ) − s(i, k ) < 50μV ⎪ s (i, k ) : otherwise. ⎪⎩

(2)

It means that those parts of the ST reference function coinciding with sudden step changes are replaced by the ST level function. By subtracting the ST reference function of the lead from the ST level function the ST deviation function, d(i,k), is finally constructed: d (i, k ) = s(i, k ) − r2 (i, k ).

(3)

An example of an ST deviation function is shown in Fig. 1.f, that resembles one constructed by the human experts (Fig. 1.c).

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A. Smrdel and F. Jager

Fig. 1 Time trends of a three-lead record s30661 (shown is the first lead) from the LTST DB (6 hour excerpt from 24 hour record is shown, starting at 10 hours after the start of the recording). Legend: (a) heart rate ([bpm]); (b) ST level function as derived by the annotators of the LTST DB ([μV]); (c) ST deviation function as derived by the annotators ([μV]); (d) ST level function as derived by the algorithm ([μV]); (e) ST reference function as derived by the algorithm ([μV]); (f) ST deviation function as derived by the algorithm ([μV]); (g) an axis shift as detected by the algorithm (a vertical tic above the line), and two ST segment episodes and two axis shifts (vertical tics below the line) as annotated by the expert annotators. Determination of lead orientation To determine the lead orientation, the samples of the ST deviation function of an ECG lead, d(i,k), are considered as samples of a random variable to construct a histogram of this function. An example of such a histogram of the first lead of record s30661 is shown in Fig. 2. Next, the z-th statistical moment above the threshold K S =50 μV: B z 1 (4) mz+ (i, K S ) = ∑ ( x − K S N (i, x)), M x=KS and z-th statistical moment below the threshold − K s : m z− (i,− K S ) =

−KS

∑ (x+K

x=− B

z S

1 N (i, x)), M

classification. To optimize the algorithm the first, second, and the third moment for various values of K C were investigated. As the optimization constraint the minimum number of leads containing only elevations as being falsely classified as those containing depressions and the minimum number of leads containing only depressions as being falsely classified as containing elevations was chosen. The optimal values for K C according to optimization constraints are: 2 × 10 3 ( μV ) for the first, 75 × 10 3 ( μV ) 2 for the second, and 3.75 × 10 6 ( μV ) 3 for the third moment. An example in Fig. 2 demonstrates detection of a lead orientation.

(5)

are constructed, where z denotes moment used, N(i,x) represents number of samples with value x in the histogram, M is number of samples of d(i,k), and B=1500 μV defines the bounds between which the histogram is constructed. From these two moments the algorithm determines the lead orientation using following rule: 1 ⎧ + − ⎪ p : if (mz (i, K S ) − mz (i,− K S ) > M K C ) ⎪ 1 ⎪ O(i ) = ⎨n : if (mz+ (i, K S ) − mz− (i,− K S ) < − KC ) M ⎪ ⎪u : otherwise, ⎪ ⎩

where p, n, and u denote positive, negative and uncertain orientation respectively, and K C is the threshold for lead

(6)

Fig. 2 Histogram of the ST deviation function of the first lead, d(1,k), of record s30661 of the LTST DB. Ischemic episodes in this lead are depressions. See text.

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An algorithm for classification of ambulatory ECG leads according to type of transient ischemic episodes

III. RESULTS Table 1 summarizes results of the ECG lead classification using the first, second and the third moment and optimal thresholds K C . The results show that the algorithm using the first, second or the third moment, correctly classified all 9 leads with elevations as having positive orientation. The algorithm, using the first moment, correctly classified 86 out of 90 leads with depressions as having negative orientation, while three leads were classified as having positive orientation, and one as uncertain. Using the second (third) moment, the algorithm correctly classified 87 (89) leads with depressions as having negative orientation, while three (one) leads were classified as uncertain. Regardless of the moment used, the classification of the leads with mixed type of episodes was poor. The classification of the leads without episodes showed, that the best results were obtained using the third moment, when the algorithm classified 15 out of 42 leads as uncertain.

37

such leads. Larger number of episodes of one type than of the other prevails in such a way that the orientation is shown as either positive or negative. The developed algorithm also did not perform well while classifying leads containing no ischemic episodes. To better evaluate leads with mixed type of episodes and leads containing no episodes the rule (6) will need to be improved and more sophisticated method for determining the lead orientation will be investigated. Although, the best results were achieved using the third moment, the choice of the moment to use is predominantly dependent on the application for which it is to be used. In applications that would follow the protocol A of the LTST DB the use of the lower amplitude thresholds might be preferable. In this case the use of the second or the first moment might be desirable. We conclude that the algorithm developed showed nice performance in classification of ECG leads, will be improved, and could be a valuable tool to determine the severeness of ischemic heart disease.

Table 1 Results of the ECG lead classification using the first, second and the third moment with optimal threshold according to the reference annotations for the protocol B of the LTST DB. See text. Positive

REFERENCES

Negative

Uncertain

1.

0 86 2 24

0 1 1 8

2.

0 87 2 20

0 3 0 12

0 89 2 18

0 1 0 15

First moment Elevations Depressions Mixed No episodes

9 3 2 10

3.

Second moment Elevations Depressions Mixed No episodes

9 0 3 10 Third moment

Elevations Depressions Mixed No episodes

9 0 3 9

IV. DISCUSSION The results showed that the algorithm developed is efficient in classifying ECG leads with only elevated or depressed transient ischemic ST segment episodes. Using the third moment, almost all leads with either elevations or depressions were correctly classified. Main reason for uncertain classification of leads with mixed types of episodes is due to the different number of episodes of each type in

4. 5. 6.

Jager F, Taddei A et al (2003) Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med Biol Eng Comput 41: 172-182. Berger PB (2006) Acute Myocardial Infarction. Acp Medicine http://www.acpmedicine.com/dxrx/dxrx0108.htm Kleber AG (2003) ST segment elevation in electrocardiogram: a sign of myocardial ischemia. Cardiovasc Res 45: 111118. Wang K et al (2003) ST-Segment Elevation in Conditions Other Than Acute Myocardial Infarction. N Engl J Med 349: 2128-2135. Smrdel A, Jager F (2004) Automated detection of transient ST-segment episodes in 24 h electrocardiograms. Med Biol Eng Comput 42: 303-311. Smrdel A (2004) Robustno avtomatsko odkrivanje prehodnih epizod segmenta ST v 24-urnih elektrokardiogramih. PhD Thesis, Faculty of Computer and Information Science, University of Ljubljana. Author: Institution: Address: City: Country: E-mail:

Ales Smrdel Faculty of Computer and Information Science Trzaska 25 Ljubljana Slovenia [email protected]

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Assessment of the Heart Rate Variability during Arousal from Sleep by Cohen’s Class Time-Frequency Distributions M.O. Mendez 1, A.M. Bianchi1 , O.P. Villantieri1 and S. Cerutti1 1

Bioengineering Department, Politecnico di Milano, Milano, Italy

Abstract— Arousal from sleep is a normal physiologic event which produces well defined changes in the sympatho-vagal balance. Arousal from sleep is related to sleep fragmentation and some sleep disorders as obstructive sleep apnea. However when repetitive arousals are found during sleep time, bad sleep quality and as consequence sleepiness during the day are associated. We studied the dynamic of the HRV during arousal accompanied by muscular activity. Ten isolated arousals free from any pathologic event where studied. Three TimeFrequency distributions (TFDs), Born-Jordan, Choi-Williams and Smooth Pseudo Wigner-Ville Distributions, were analyzed in order to evaluate their performance during arousal episodes. The three TFDs showed the same performance when analytic HRV signal is used. LF component suggests a major participation of the sympathetic activity at the beginning of the arousal episode while HF component suggests a major role of the parasympathetic drive at after arousal episode. Keywords— Sympatho-vagal balance, Time-Frequency Distribution, Obstructive Sleep Apnea.

I. INTRODUCTION Sleep is an unconscious process where the human being interacts with his inner world and interaction with real worldis basically vanished. When we have a restorative sleep, we wake with energy to do all our daily tasks. However, sleep can be disturbed by different causes that range from stress until physiological pathologies as obstructive sleep apnea. Arousal from sleep is a natural physiological event that appears during both the normal sleep and pathologic episodes. Arousal from sleep has a very close relationship with sleep fragmentation since repetitive arousals from sleep produce a low sleep quality. Classical symptoms caused by a disrupted normal sleep are daily sleepiness, memory impairment and low concentration. In addition when arousal from sleep are related to noxious respiratory pathologies as obstructive sleep apnea, normal physiological levels can be altered even during wakefulness. Consequences of diurnal hypertension until heart failure can occur. Arousals from sleep have two main functions, activating sensory organs to monitor the environment and bolstering physiological system up to overcome noxious events [1]. Arousal from sleep is defined from the cortical electroencephalogram waves. Some definitions can be found in literature were spindles, k-complexes are part of

the arousal hierarchy as arousal precursors. An arousal episode is defined as a sudden increment in the EEG waves with at least thee seconds and not superior to 10 seconds. In addition, the shift frequency includes theta, alpha or frequencies higher that 16 Hz. In REM sleep an arousal must be accompanied by muscular activity. A complete arousal description is found in [2]. Arousals from sleep produce changes in heart rate. Heart rate presents increment during arousal episodes due to a withdrawal of the parasympathetic flow and a strong activation of sympathetic activity. Effects of arousal from sleep in sympatho-vagal balance have been widely studied as during NREM sleep and with inducted arousals [1,3-4]. On the other hand, in the past decades has been found a tie relationship between the autonomic nervous system and the spectral components of the heart rate variability. Therefore, methods of spectral decomposition represent a non invasive tool to evaluate the behavior of the of the autonomic nervous system. Heart rate fluctuations range between 0 and about 0.5 Hz. Frequency range between 0.15 – 0.5 Hz is directly correlated with the vagal flow, while range between 0.02 – 0.15 represent mainly the sympathetic activity [5]. The classical method to analyse the spectral components of a signals is the Fourier transform. However, this approach does not take in account the temporal evolution of the spectral components. In addition, arousal episodes produce a transitory reflex of the sympathetic muscle which is reflected as a non stationarity in the heart rate. Approaches as Wavelets transform, Time-variant autoregressive models and Time-Frequency Distributions (TFD) allow to evaluate the temporal dynamic of the signal fluctuation and are suitable to evaluate transitories changes as those present in the heart rate variability during arousal events. TFD evaluate the Fourier transform of the temporal autocorrelation. In other words, they evaluate the Fourier transform of the autocorrelation function without expected operator (Wigner-Ville Distribution). This evaluation enables to the TFD to capture the evolution of the spectrum at each sample, but in multi-component signals spurious frequency components appear. In order to reduce the spurious terms, some smooth functions (Kernels) have been incorporated inside to the Wigner-Ville Distribution. Each kernel represent a Time-Frequency Distribution and attain to specific properties such as time-frequency invariance, finite support, etc. Cohen’s class TFD is a well defined group of

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Assessment of the Heart Rate Variability during Arousal from Sleep by Cohen’s Class Time-Frequency Distributions

kernels that satisfy the time-frequency invariance property. This means that if a signal x(t) is delayed in time or modulated in frequency then its TFR will be delayed in time and shifted in frequency. This is an important property when physiological signals are analyzed [6]. The aim of this study is evaluate beat-by-beat the dynamic evolution of the autonomic nervous system. BornJordan, Choi-Williams and Smooth Pseudo Wigner-Ville Distribution which are part of the Cohen’s class are tested on synthetic signal and HRV sequence during arousal episode. We evaluated the goodness of each distribution to assess the dynamic changes of the HRV fluctuation in arousal episodes. II. MATERIAL AND METHODS Five overnight polysomnographic recordings were obtained from five healthy subjects. The subjects were 48±5 year old, Body Mass Index was 36±2 Kg/m2. Data were obtained using a polymnosograph Heritage Digital PSG Grass Telefactor. All signals were acquire with a sampling frequency of 100 Hz. Sleep stages were evaluated according to the standard clinical criteria [7]. Arousals were identified from EEG C4/A1 channel during stage 2 by expert personal, in agreement with the definition given in [2]. Ten arousal with muscular activity were selected free of noise and distant of any pathologic event (OSA or PLM). Therefore, the RR intervals were searched and detected from ECG channel by a derivative algorithm. Due to the low sampling frequency a better detection of R peaks was obtained by a parabolic interpolation. RR time series were verified and manually corrected where misdetections occurred and when extrasystoles happened the corresponding portion of the signal was discarded. Intervals of 2 min and 30 sec as baseline and the same interval as recovery after arousal were taken. A. Method Development Cohen’s Class Time-Frequency Distributions (TFD) of a signal x(t) is defined as:

⎛ τ⎞ ⎛ τ⎞ C x (t , f ) = ∫∫ φ (t − t ' ,τ ) x * ⎜ t '− ⎟ x⎜ t '+ ⎟e − j 2πfτ dt ' dτ ⎝ 2⎠ ⎝ 2⎠ (1) Where φ (θ ,τ ) is a function labeled kernel. By choosing different kernels, different features of the distributions are obtained. From here there is an infinite number of distributions that can be obtained. The TFD are defined according

31

to the kernel. Smooth Pseudo Wigner-Ville Distribution is characterized by independent smoothing functions, in time and in frequency originated by γ (t ) and η ⎛⎜ τ ⎞⎟η * ⎛⎜ − τ ⎞⎟ ⎝2⎠ ⎝ 2⎠ windows respectively, where the kernel function is:

⎛τ ⎞ ⎛ τ ⎞ ϕ ( t ,τ ) = γ ( t )η ⎜ ⎟η * ⎜ − ⎟ ⎝2⎠

⎝

2⎠

(2)

Choi and Williams Distribution is defined as:

ϕ ( t ,τ ) =

⎡ σ σ 1 exp ⎢ − 4π τ ⎣⎢ 4

2 ⎛t⎞ ⎤ ⎜ ⎟ ⎥ ⎝ τ ⎠ ⎦⎥

(3)

The scaling factor σ determines the cross-terms suppression, time and frequency resolution and concentration of auto-terms. High value of σ gives a good definition of autoterms and low cross-terms suppression, while low values of σ reduces cross-terms and spread out the auto-terms. Born-Jordan Distribution (BJD) maintains most of the attractive properties due to associates mixed products of time and frequency. The distribution had been used as bases for creating other distributions, or used for evaluating new distributions proposed. BJD is defined as:

⎧1 ⎪ , ϕ (t ,τ ) = ⎨ τ ⎪0, ⎩

t / τ <1/ 2 (4)

t / τ >1/ 2

Arousal from sleep produce a strong and rapid change in the HRV signal. We are interesting in analyzing the ability of this TFD to follow this fast changes. In order to test the capacity of the different TFD we generate a tri-component synthetic real signal in the following way:

⎧sin(2πf1n), 32 ≤ n ≤ 192 ⎪sin(2πf n), 193 ≤ n ≤ 319 ⎪ 2 i= ⎨ ⎪sin(2πf 3 n), 320 ≤ n ≤ 480 ⎪⎩0, otherwise

(5)

Where f1=0.1 Hz, f2=0.25 Hz and f3=0.4 Hz In order to reduce the cross terms produced by the negative frequencies, Hilbert Transform was applied to the synthetic sequence. The sampling frequency of the synthetic signals was 4 Hz and 512 samples. The TFR parameters for SPWV were : smooth time window, hamming 21 samples; smooth frequency window Hamming 129 samples. For CWD a fixed σ = 1. Three frequency bands, A (0.35 –

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M.O. Mendez, A.M. Bianchi, O.P. Villantieri and S. Cerutti

0.45 Hz), B (0.15 – 0.25 Hz) and C (0.05 – 0.1 Hz) across all time were created in order to have a clear indication of the instantaneous power in each band. These bands show the interference terms attenuation and negative components. Each band and the total frequency range (0-0.5 Hz) were integrated across the frequency axis with the intention of comparing the real time evolution of the instantaneous power of the signal and the one obtained with the TFR. The results of the TFRs are showed by means of an image, the energy range is plotted in a gray scale of 256 values. B. Arousal Data Resulting RR sequences were re-sampled at 2 Hz by cubic spline interpolation and detrended. Therefore, to each RR sequence was applied Hilbert Transform in order to obtain an analytic signal. After that, SPWVD was used to obtain the evolution of RR power at different frequencies and times. Then, it was computed the time evolution of the classical heart rate variability indexes: (PT) total power (0.005-0.5 Hz), (VLF) very low frequency (0.005-0.04 Hz) ; (LF) low frequency (0.04-0.15 Hz); (HF) high frequency (0.15-0.4 Hz); and low to high frequency ratio (LF/HF). All spectral powers were computed in absolute units. The representation and spectral indexes was obtained using the absolute values of the distribution. The data were synchronized with the occurrence of the minimum RR value. Thereafter, an ensemble average was obtained for each spectral index and RR intervals. Each sequence was normalized as the percentage of change respect to the mean of the first 20 seconds of each sequence. All the indexes values were given as mean ± standard deviation. Segments of 180 points from data and spectral indexes were analyzed. III. RESULTS Figure 1 shows three sinusoids at different times and frequencies according to Equation 5. Signal components are clearly separated and fine represented by the three distributions in the time-frequency plane (upper panel). The results are so similar that is almost impossible distinguish among them by their time-frequency representation. From the middle panel, we can observe that the instantaneous power in A, B and C bands is totally smoothed by the three distributions. Finally, when we compare the instantaneous power of the real signal and the instantaneous power evaluated from the timefrequency energy plane, all TFDs follow in mean the real instantaneous power (lower panel). However, most important is to recognize that TFDs followed with high time resolution

Figure 1. Time-Frequency analysis of a synthetic signal composed by three sinusoids after the Hilbert transformation. The first row shows the time evolution of the PSD of the signal obtained by three Time-Frequency representations, Smooth Pseudo Wigner-Ville Distribution (SPWVD), ChoiWilliams Distribution (CWD) and Born-Jordan Distribution (BJD). The second row depicts the instantaneous frequency for A (dashed line), B (black line) and C (gray line) frequency bands. The third row present the instantaneous power for the whole frequency axis, gray line represents the theoretical instantaneous power while black line is the one obtained after integrating the PSD respect to the frequency axis.

the changes the changes in frequency presented in the synthetic signal. Figure 2 presents mean and SE of time evolution of the spectral indexes of HRV for arousals from sleep during stage two sleep. The series are presented as percentage variations respect to the baseline (see method). RR intervals present a fast decrement which reaches its minimum value around seven seconds after the beginning of the arousal episode, after that it begins a recovery phase over-passing the baseline and returning to the base line 20 seconds later. RR intervals, have significant lower values respect to the base line during 9 to 15 seconds. HF component has a decrement immediately when an arousal happens during the first 3 seconds, then a constant increment which arrive at the maximum value close at the final of the arousal. However, no significant difference were found. LF component presents a strong increment during arousal episode which shows its maximum value close to RR minimum and go back to baseline levels after 25 seconds. Significant differences respect to baseline are from 9 to 20 second. VLF component and LF to HF ratio showed an analogous performance as the LF component.

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Assessment of the Heart Rate Variability during Arousal from Sleep by Cohen’s Class Time-Frequency Distributions

33

but a smoothed version of this one (see Figure 1). When we deal with the HRV signal we are interested in the instantaneous frequency which describes the relations of the sympatho-vagal balance. Then, when we use an analytic version of a HRV signal it is indifferent to use any distribution. Changes in the sympatho-vagal balance during an arousal event are produced by both an increment in the symphatetic activity (LF increment) and a reduction in the parasympathetic activity (HF reduction). This fast and large change is caused by the activation of the sympathetic activity while the vagal tone seem not play an important role during the episode. On the Contrary, at the arousal end a tachycardia is observed where the vagus nerve play a major role. In conclusion TFDs are a fine tool to evaluate the dynamic that the autonomic nervous system presents during an arousal episode. When we work with analytic version of the HRV, the results suggest that it is indifferent applied any TFD. The sudden tachycardia observer at the beginning of an arousal seems caused mainly by the sympathetic activation. In the tachycardia after arousal episode parasympathetic activity seems to play a major role. Figure 2. Time evolution indexes of the heart rate variability parameters during arousal from sleep episode. Spectral indexes were evaluate by Smooth Pseudo Wigner-Ville distribution. Values are presented as mean value and standard error of the percentage changes respect to the baseline. Form the top to the bottom. RR intervals (RR), High frequency (HF), Low frequency (LF), Very low frequency (VLF) and Low to high frequency ratio (L/H). The window with * represents significant differences.

IV. DISCUSION AND CONCLUSIONS Born-Jordan, Choi-Williams and Smooth Pseudo Wigner-Ville distributions belonged to Quadratic Cohen Class Time-Frequency Distributions are used to evaluated the behavior of the autonomic nervous system during arousal form sleep episodes. Our main observations are : a) the three TFDs allow to evaluate with large time-frequency resolution the behavior of the heart rate variability even during transitory events. b) When we used an analytic signal, applying any of these TFDs during arousal episodes is indifferent. c) Arousal episodes produce a high increment in the LF/HF balance during arousal. However this change is mainly caused by a rise in LF. Physiological signals are real in nature. However, when we work with Time-Frequency distribution is recommended to use analytic signals in order to reduce the interference terms generated by the quadratic nature of this approach. Nevertheless the interference terms are necessary to retain fine properties found in BJD and CWD such as finite time support and marginals. Those properties are lost when we use analytic signals. If we integrate respect to the frequency axis, we do not obtain the instantaneous power of a signal

ACKNOWLEGEMENT This work was supported by the European project MY HEART

REFERENCES 1. 2. 3. 4. 5. 6. 7.

Halasz P, Terzano M. Liborio P, Bodizs R (2004) The Nature of Arousal in Sleep. J Sleep Res 13:1-13. Atlas Task Force of American Sleep Disorders Association (1992) EEG arousal: scoring rules and examples. Sleep 15: 174-184. Catcheside, et. al (2001) Acute Cardiovascular Responses to Arousal from Non-REM Sleep During Normoxia and Hypoxia. Sleep 24:895-902. Blasi A et.al. (2003) Cardiovascular Variability after Arousal from Sleep: Time-Varying spectral Analysis J Appl Physiol 95:1394-1404. Malliani A (1999) The Pattern of Sympathovagal Balance Explored in the Frequency Domain. News Physiol Sci 14: 111-117. Cohen L (1989) Time-Frequency Distributions – A Review. Proc. IEEE (77) 941-981. Rechtschaffen A, Kales AE (1968) A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Brain information Services/Brain Research Institute, UCLA, 1968. Author: Institute: Street: City: Country: Email:

Martin Mendez Politeci di Milano P.zza Leonardo da Vinci 32, 20133 Milan Italy [email protected]

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Autonomic Modulation of Ventricular Response by Exercise and Antiarrhythmic Drugs during Atrial Fibrillation VDA Corino1,2, LT Mainardi1, D Husser2, A Bollmann2 1

2

Department of Biomedical Engineering, Politecnico di Milano, Milano, Italy Department of Cardiology, University Hospital Magdeburg, Magdeburg, Germany

Abstract— Ventricular response (VR) during atrial fibrillation (AF) is a complex process that is influenced by the autonomic nervous system (ANS). Although antiarrhythmic drugs are known to have modulating ANS effects, these are not routinely evaluated in the clinical setting. Therefore, the purpose of this study is to characterize VR to exercise as ANS stimulus during AF and to evaluate possible modulating effects of antiarrhythmic drugs (flecainide and amiodarone). Seventeen patients (11 males, mean age 61±11 years), with persistent AF, underwent bicycle exercise testing before and after 3 – 5 days of oral flecainide (9 patients) or amiodarone (8 patients) loading. RR series were derived from ECG recordings and analyzed by means of time domain parameters (mean, SDNN, pNN50 and rMSSD) and non-linear methods assessing the predictability of the time series (level of predictability (P) and regularity (R)). The effect of exercise in VR modulation was evident both with and without antiarrhythmic drugs (p<.05). Both antiarrhythmic drugs had no significant effects at rest but on exercise conditions: flecainide decreased pNN50 and rMSSD, while amiodarone increased the mean of NN interval. In addition, flecainide amplified the response to exercise, resulting in a pronounced pNN50 reduction, while no differences were observed with amiodarone administration. In conclusion, antiarrhythmic drugs exhibit ANS modulating effects during exercise that are not apparent during resting conditions. Keywords— atrial fibrillation, autonomic nervous system, flecainide, amiodarone, exercise testing.

I. INTRODUCTION Atrial fibrillation (AF) is characterized by an irregular ventricular response (VR) that is often described as chaotic and without any form of patterning. Previous studies have shown, however, that this process is not completely random [1] [2]. Moreover, a reduced VR variability may predict an adverse prognosis in patients with advanced heart failure [3]. The autonomic nervous system (ANS) [4] is one of the most important factors influencing VR in AF. Its effect is largely mediated by the modulation of atrioventricular node refractoriness, which is mainly dependent on vagal tone [5]. Exercise is associated with complex responses of sympathetic and vagal control mechanisms, and represents consequently a natural model to evaluate the effects of autonomic modulation on VR dynamics.

In many patients antiarrhythmic drugs are prescribed to terminate AF and to prevent its recurrence [6]. It is well known that in addition to the desired electrophysiological effects, these drugs may influence autonomic control mechanisms. However, these effects are not routinely evaluated in the clinical setting. Thus, the purpose of this study was twofold: (1) to characterize VR in response to changes of the autonomic balance induced by exercise testing, and (2) to elucidate the influence of two commonly used antiarrhythmic drugs, flecainide and amiodarone, on the dynamics of VR at rest and during exercise in patients with persistent AF. II. METHODS A. Study protocol Seventeen patients (11 men/6 women, mean age 61 ± 11 years), with persistent AF (mean AF duration 21 ± 29 months) and referred for cardioversion, were included in this study. Echocardiographic characteristics were: left atrial diameter 47 ± 5 mm and left ventricular ejection fraction 56 ± 15 %. The pharmacological therapy included digitalis in 8 patients, calcium channel blockers in 4 and beta blockers in 6 (more than one drug is possible for each patient). Patients underwent symptom-limited bicycle exercise stress testing using a 3-minute step-up protocol. Workload increase was chosen according to age- and gender-predicted values, aiming for a test duration of 8 to 12 minutes. Exercise testing was repeated after 3 – 5 days of flecainide (in 9 patients, 200 mg/day) or amiodarone (in 8 patients, 1200 mg/day) loading. An ECG (Predictor, Dr. Kaiser; sampling rate 2 KHz) was continuously recorded with the subject in supine position at baseline and immediately after termination of exercise. Thus, four clinical conditions were defined for each patient (Figure 1). The QRS detection and RR interval measurements were automatically performed on two-minute segments at baseline and immediately after termination of exercise. RR interval series were visually checked and missed/misdetected beats were corrected using an interactive software. Prema-

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Autonomic Modulation of Ventricular Response by Exercise and Antiarrhythmic Drugs during Atrial Fibrillation

Fig. 1 Protocol phases ture ventricular contraction or beats with aberrant conduction were excluded from the time domain analysis, while they were substituted by linear interpolation of the preceding and the subsequent RR intervals for the series predictability analysis [7]. B. VR analysis Time domain parameters: Time domain parameters were computed following the recommendations for heart rate variability measurements [8] that have also been applied during AF [9]. Time domain analysis includes the mean (M) and the standard deviation (SDNN) of all normal-to-normal intervals (NN), the root of the mean squared differences of successive NN intervals (rMSSD) and the percentage of interval differences of successive NN intervals greater than 50 ms (pNN50). Level of predictability: A discrete time series x(n) can be modeled as the output of an autoregressive model of p order p

x(n) = ∑ a k x(n − k ) + w(n)

(1)

k =1

where n is the discrete-time index, the ak are the model coefficients and w(n) is a Gaussian white noise process of variance σ2 feeding the model. The actual sample differs from its model prediction, thus generating the prediction error p

e( n ) = x ( n ) − ∑ a k x ( n − k )

(2)

k =1

An index of the level of predictability (LP) may be defined as follows LP = (1 −

σe ) ⋅100 σx

(3)

where σe is the standard deviation of e(n) and σx is the standard deviation of the process x. LP measures the percentage of standard deviation which may be predicted by the autoregressive model. In the case of a purely random signal (σe

83

is quite close to σ x ) LP tends to 0, while in the case of a linearly predictable signal (σ e tends to zero) the index tends to 100% and it assumes intermediate values for those processes that may be partially predicted from the model. Regularity: The regularity (R) index is related to the degree of recurrence of a pattern in a time series and it is based on the Conditional Entropy (CE), i.e. the amount of information carried by the most recent sample x(i) of a normalized realization of x when its past L-1 samples are known. For a given signal, M different patterns of length L can be obtained (the J-th of them is indicated as xLJ ). CE is defined as [10] N

CE ( L) = −∑ p( xi / x LJ −1 ) log( p( xi / x LJ −1 ))

(4)

i =1

where p(xL-1J) represents the probability of the pattern xL1(i) and p(xi/xL-1) the conditional probability of the sample x(i) given the pattern xL-1. The estimation of CE(L) is no longer statistically consistent [10] when L increases, therefore the Corrected Conditional Entropy (CCE), sum of CE(L) and a corrective term, must be introduced to perform a reliable measure over short data series. The CCE is then normalized by the Shannon entropy of the process to derive an index independent of the different probability distribution of the processes, obtaining the Normalized Corrected Conditional Entropy (NCCE). The R index may be defined as: R = 1 − min( NCCE ( L))

(5)

In case of a purely periodic signal (a fully predictable process) the observation of a few samples makes it possible to predict the successive ones (i.e. the new samples are not carrying any new information and simply replicate the previous ones). In general, R tends to 0 if the series is a fully unpredictable process, it tends to 1 if the series is a periodic signal and it assumes intermediate values for those processes that can be partially predicted by the knowledge of the past samples. Statistical analysis: Data are reported as mean ± one standard deviation. Rest vs. exercise at baseline and after drug loading was compared using Student’s t-test for paired data. In order to assess the response to exercise, differences between exercise and rest condition were computed and compared for the drug-free state and after drug loading using Student’s t-test for paired data. To evaluate the differences between the two antiarrhythmic drugs, Student’s t-test for unpaired data was used. A p value of at least .05 was considered statistically significant.

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III. RESULTS A. Effects of Exercise on VR Exercise effects on VR modulation were evident in patients with and without antiarrhythmic drug administration, with time domain and non-linear methodologies. A significant decrease (p<.001) was noted in all time domain parameters, reflecting a reduction in the RR beat-tobeat variability. In addition, an increase (p<.05) of the LP and R indexes, reflecting linear and non-linear series predictability respectively, were observed (Tables 1 and 2). Table 1

Mean values ± one SD during baseline and flecainide administration Baseline

M SDNN pNN50 rMSSD LP R

Rest 689 ± 145 156 ± 43 74 ± 13 224 ± 73 1.63 ± 0.93 0.06 ± 0.06

Exercise 635 ± 93** 119 ± 38** 71 ± 12 164 ± 56** 2.85 ± 4.03 0.07 ± 0.07

Flecainide Rest 702 ± 121 161 ± 55 80 ± 6 227 ± 84 1.31 ± 0.8 0.06 ± 0.05

Exercise 525 ± 93** 112 ± 44** 55 ± 22* 140 ± 58** 5.27 ± 4.21* 0.15 ± 0.17*

* p<.05 † p<.01 exercise effect

Table 2

Mean values ± one SD during baseline and amiodarone administration

Baseline M SDNN pNN50 rMSSD LP R

Rest 775 ± 156 200 ± 52 85 ± 9 288 ± 87 2.6 ± 2.8 0.05 ± 0.03

Amiodarone Exercise 565 ± 164† 143 ± 40† 69 ± 10† 174 ± 59† 9.1 ± 10.0* 0.09 ± 0.08

Rest 826 ± 177 191 ± 53 82 ± 9 276 ± 80 2.3 ± 1.4 0.05 ± 0.04

Exercise 596 ± 176† 149 ± 51† 68 ± 12† 183 ± 78† 12.5 ± 16.8 0.16 ± 0.15*

* p<.05 † p<.01 exercise effect

B. Antiarrhythmic Drug Effects Both flecainide and amiodarone did not alter VR parameters under resting conditions. However, significant differences in a few parameters were noted under exercise conditions, as shown in Tables 1 and 2. In particular, after flecainide loading pNN50 and rMSSD decreased, while the mean of NN increased with amiodarone. In addition, the differences between parameters with and without drugs were computed and compared. Some significant changes were noted during exercise and are shown in Figure 2. Flecainide decreased M, pNN50, rMSSD, while amiodarone increased those parameters (p<.05).

Fig. 2 Box plots of the difference (d) with drug vs. without drug during exercise phases. Only VR parameters with significant differences are shown. See text for details. * p< .05

To assess drug effects on VR to exercise, exerciseinduced changes of VR parameters at baseline and after drug administration were compared. After amiodarone administration the change in all assessed variables was similar compared with baseline measurements. On the contrary, flecainide amplified the response to exercise, resulting in a pronounced (p<.05) pNN50 reduction (Figure 3). IV. DISCUSSION In this study, VR has been characterized according to the changes of the autonomic balance induced by exercise testing, in patients with persistent AF. In addition, the effect of two antiarrhythmic drugs, flecainide and amiodarone, during rest and exercise and their influence on exercise response, has been assessed.

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Autonomic Modulation of Ventricular Response by Exercise and Antiarrhythmic Drugs during Atrial Fibrillation

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derived from time domain and non-linear analysis. This may be useful for better characterization of AF modulating factors in the individual patient and assessment of antiarrhythmic drug effects.

ACKNOWLEDGMENT This study was supported by the Volkswagen Foundation.

REFERENCES

Fig. 3 Mean ± one SD and individual values of pNN50 in flecainide group. See text for details. 1.

A. Exercise effects After exercise, a significant decrease was noted in all time domain parameters (i.e. mean RR, SDNN, pNN50 and rMSSD) evidencing a reduction in the RR beat-to-beat variability. In addition, an increase of the LP and R indexes, reflecting linear and non-linear series predictability respectively, was observed. It should be noticed that both the LP and R values are very low compared to sinus rhythm [11], thus the predictability degree of VR is very small. Nevertheless, both the parameters succeed in underlining the increased predictability of VR during exercise. These results regarding VR during exercise stress the relevant role that is played by the ANS in patients with AF. These findings are consistent with an increase of the sympathetic tone and decrease in vagal tone. B. Drug effect During rest condition, the computed parameters were not different after drug (flecainide or amiodarone) administration, while during exercise testing some significant differences were observed. Flecainide decreased pNN50 and rMSSD, thus suggesting a possible enhanced vagolytic effect, as both these indexes are highly related to vagal activity. Amiodarone increased the mean of NN intervals, thus highlighting its antiadrenergic activity. Concerning these three parameters, they are reduced after flecainide administration, while they are increased after amiodarone loading. Thus, this distinct behavior stresses the differences in ANS modulating effects of the two different classes of antiarrhythmic drugs. C. Conclusions Monitoring of exercise-induced and antiarrhythmic drug effects on the ANS during AF is possible with parameters

Stein KM, Walden J, Lippman Net al. (1999) Ventricular response in atrial fibrillation: random or deterministic? Am J Physiol 277:H452H458. 2. Hayano J, Yamasaki F, Sakata S et al. (1997) Spectral characteristics of ventricular response to atrial fibrillation. Am J Physiol 273:H2811H2816. 3. Frey B, Heinz G, Binder T et al. (1995) Diurnal variation of ventricular response to atrial fibrillation in patients with advanced heart failure. Am Heart J 129:58-65. 4. Nagayoshi H, Janota T, Hnatkova K et al. (1997) Autonomic modulation of ventricular rate in atrial fibrillation. Am J Physiol 272:H1643H1649. 5. Toivonen L, Kadish A, Kou W et al. (1990) Determinants of the ventricular rate during atrial fibrillation. J Am Coll Cardiol 16:11941200. 6. Husser D, Stridh M, Sornmo L et al. (2005) Time-frequency analysis of the surface electrocardiogram for monitoring antiarrhythmic drug effects in atrial fibrillation. Am J Cardiol 95:526-528. 7. Huikuri HV, Niemel MJ, Ojala S et al. (1994) Circadian rhythms of frequency domain measures of heart rate variability in healthy subjects and patients with coronary artery disease: effects of arousal and upright posture. Circulation 90:121–126. 8. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043-1065. 9. Van den Berg MP, Haaksma J, Brouwer J et al. (1997) Heart rate variability in patients with atrial fibrillation is related to vagal tone. Circulation 96:1209-1216. 10. Porta A, Baselli G, Liberati D et al. (1998) Measuring regularity by means of a corrected conditional entropy in sympathetic outflow. Biol Cybern 78:71-78 11. Mainardi LT, Corino VDA, Lombardi L et al. (2004) Assessment of the dynamics of atrial signals and local atrial period series during atrial fibrillation: effects of isoproterenol administration. Biomed Eng Online 3:37. Author: Ing. Valentina D.A. Corino Institute: Dipartimento di Ingegneria Biomedica Politecnico di Milano Street: via Golgi 39 City: 20133, Milano Country: Italy Email: [email protected]

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Classification Methods for Atrial Fibrillation Prediction after CABG S. Sovilj1, R. Magjarević1 and G. Rajsman2 1

University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia 2 Clinical Hospital Center Zagreb, Department of Cardiac Surgery, Zagreb, Croatia

Abstract— The aim of this study is to compare different methods for the classification type problems specifically in predicting Atrial Fibrillation (AF) after Coronary Artery Bypass Grafting (CABG). The prediction/classification model tends to predict a categorical dependent variable (which determines the belonging of patient to a group of patients that have or to a group of patients that have not developed AF), by one or more continuous and/or categorical predictor variables derived from the patients' history, electrocardiograms and in particular from the P wave. We have obtained the parameters from continuously recorded ECG after the surgery. Keywords— classification, prediction, atrial fibrillation, CABG, discriminant analysis, decision tree.

I. INTRODUCTION Atrial fibrillation (AF) is the most common postoperative arrhythmia after CABG and occurs in 30 – 40 % patients [1]. AF may cause different complications like hemodynamic changes, cerebral and other thromboembolisms etc., all potentially dangerous for patients. In previous studies which tried to find predictors of AF in similar groups of patients, authors analyzed relatively short segments of multichannel ECG recorded and included into analysis also some other measured physiological parameters or data from patient history [2, 3]. These studies have not brought to consent on either parameters which should be recorded or on their value which could be used in clinical practice as a general procedure. Therefore we decided to analyze the parameters of the patients’ ECGs recorded continuously after the surgery (while the patients are still in the ICU). We have enlarged the number of parameters from the ECG that entered the analysis and we used less demanding instrumentation for ECG acquisition. The aim of this study is to develop a statistical classification model for prediction of AF based on parameters obtained from the patients' ECGs which were continuously acquired after the CABG. The early risk assessment for AF, several hours before it begins, would result the timely medication treatment of patients prone to AF and would reduce an incidence of arrhythmia while the other group of patients could be excluded from prophylactic anti-arrhythmia medication, thus reducing possible drug contraindications in the group.

II. METHODS In the period from 2005 to 2006, we have continuously recorded the standard II lead ECG of fifty patients in a period of typically 48 hours after CABG or until the onset of AF. The ECG was acquired with HP Patient Monitor 78330A and digitized using a standard ADC card (Measurement Computing CIO-DAS08/JR) embedded into a PC. The sampling frequency was 1kHz and the amplitude resolution 12-bit. Electrocardiograms were segmented with a QRS and P wave detector based on the wavelet transformations [2] and numerous parameters were calculated especially in the P wave segment of ECG. Every recorded ECG was divided in the time segments of one-hour duration. In each one-hour ECG record, the number of detected QRS segments had to be more than 2000 in order to exclude those segments which had a lot of artifacts superimposed to the signal. Furthermore the records in which a number of detected P waves was less than 75% of the total number of detected QRS segments in current hour were also excluded from analysis. We made statistical analysis of the data obtained from the recorded ECG segments and identified parameters which best discriminate the two patient groups (AFP and NAFP). Different AF prediction/classification models were proposed and we compared their accuracy and possible applicability: linear discriminant analysis models, classification and regression tree (C&RT) and CHAID statistical tree models, Boosting Tree Classifier models and Binomial Logit Regression models. For the statistical analysis Statistica 7, StatSoft Inc. was used [4]. A. Measured ECG parameters After the QRS and P wave detection in recorded ECG segments, 110 different parameters dealing predominantly with atrial activity were measured or calculated. Only those that were considered important in the discrimination between the groups are presented in Table 1 though later analysis excluded some of them from the models. An hourly average value and standard deviation for each of these parameters was calculated. The parameters were aligned in three categories: Time parameters, Wavelet parameters and Other parameters, as presented in Table 1.

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Classification Methods for Atrial Fibrillation Prediction after CABG

Table 1 Parameter Time parameters: PonPoffAVR, PonPoffDEV PonPpeakAVR, PonPpeakDEV PpeakPoffAVR, PpeakPoffDEV PpeakRpeakAVR, PpeakRpeakDEV PonQonAVR, PonQonDEV RRAVR, RRDEV, HRAVR, HRDEV Wavelet parameters: Pslope1AVR5, Pslope1DEV5

Pslope2AVR5, Pslope2DEV5

Pslope1Pslope2AVR5, Pslope1Pslope2DEV5 WenergyAVR5, WenergyDEV5 relWenergyAVR5 Wentropy relPslope1Pslope2AVR, relPslope1Pslope2DEV Other parameters: ampAVR, ampDEV AonoffAVR, AonoffDEV RecordHour PpeakRpeakAVR_RR, PpeakRpeakDEV_RR, PonQonAVR_RR, PonQonDEV_RR, PonPoffAVR_RR, PonPoffDEV_RR

Measured parameters. Description P wave duration (from P wave onset to P wave offset) 1st half P wave duration (from P wave onset to P wave peak) 2nd half P wave duration (from P wave peak to P wave offset) PR interval duration (from P peak to R peak) PQ interval duration (from P onset to Q onset) RR interval duration and Heart Rate

P wave rising slope (value of wavelet coefficient detected at the 5th wavelet scale) P wave falling slope (value of wavelet coefficient detected at the 5th wavelet scale) duration between points of highest and lowest P wave slope energy measured between P wave onset and offset at 5th wavelet scale relative P wave energy (ratio between energy at 5th wavelet scale and total) measure of P wave energy dispersion at different wavelet scales relative ratio between rising and falling P wave slope P wave amplitude Surface area below P wave number of hour after CABG PR interval duration normalized with RR interval PQ interval duration normalized with RR interval P wave duration normalized with RR interval

suffix AVR notes mean value of measured parameter in 1 hour period suffix DEV notes standard deviation of measured parameter in 1 hour

Statistical analysis included 360 hours of ECG of patients who developed AF and 1003 hours of ECG of patients which did not develop AF. Approximately two third of the cases (930 hours) were randomly selected and entered in the analysis as a learning sample and the remaining third of the cases (433 hours) was used for a cross-validation and was treated as a testing sample. Prior probability of the class size was estimated from learning sample.

47

B. General discriminant analysis model General Discriminant Analysis (GDA) is a method for building a multivariate linear model used to determine the variables that discriminate between two or more naturally occurring groups. A categorical dependent (criterion) variable labeled AF determines the belonging of a patient to either the group that has developed (PAF) or has not developed AF (nPAF) and it was predicted with more continuous independent (predictor) variables using the model obtained by GDA. Using GDA, a model for prediction of AF based on a number of parameters measured from ECG was built (labeled the model GDA1). In a forward stepwise analysis (in every step) all variables were evaluated in order to determine which contribute the most to the discrimination between two groups and were included into the model step by step. In each step, variables that had a statistical significance p < 0.05 in the discrimination entered into the model GDA1 while the others were removed from the discriminant function. Finally, 16 variables have been entered in the discriminant function. Variables that contribute the most to the discrimination are given in order of their importance: RRAVR, PonPoffAVR,, PonQonAVR, PpeakRpeakAVR, Pslope2AVR5, Pslope2DEV5, PonPoff23AVR (for more the detailed description of noted predictors see Table 1). It is still difficult to interpret the model and to explain why the observations are classified or predicted in a particular manner. Particularly, GDA1 model assumes a linear relation between predictor variables and dependent variables. The quality measures for the model GDA1 are presented in the Table 2. C. General classification and regression tree model General Classification and Regression Tree algorithm (GC&RT) is used to build a classification tree for predicting Table 2

Quality measures for GDA1 model (TP – true positive, FN – false negative, TN – true negative, FP – false positive). learning sample

TP cases FN cases TN cases FP cases sensitivity specificity positive predictivity negative predictivity accuracy

138 109 637 46 55,9% 93,3% 75,0% 85,4% 83,3%

testing sample 47 65 289 32 42,0% 90,0% 59,5% 81,6% 77,6%

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S. Sovilj, R. Magjarević and G. Rajsman

Table 3 TP cases FN cases TN cases FP cases sensitivity specificity positive predictivity negative predictivity accuracy

Quality measures for CRT1 model. learning sample

testing sample

140 107 645 38 56,7% 94,4% 78,7% 85,8% 84,4%

42 70 291 30 37,5% 90,7% 58,3% 80,6% 76,9%

Table 4 Quality measures for CHAID model labeled CHAID1.

Fig. 1 CRT1 classification tree model. N denotes total number of cases that entered the classification tree at certain node, PAF denotes cases (hours) the tree classified as belonging to a patient prone to AF, nPAF denotes cases (hours) the tree classified as belonging to a patient not prone to AF, Pi denotes the name of the predictor (see Table 1 for description), TP, TN, FP, FN same abbreviation as in Table 2, numbers above TP, TN, FP, FN designate the validity of the classification in the particular node.

a categorical predictor variable. GC&RT algorithm determines a set of if-then logical, univariate split conditions and tries to achieve maximal possible accuracy for the prediction. Tree models are nonparametric and nonlinear and can reveal a non-monotonic relationship between the variables using multiple splits on the same variable. The interpretation of results summarized in a tree is very simple and this simplicity is useful for a rapid classification and also for physiological evaluation and interpretation. [4]. A misclassification cost was assumed equal for both PAF and nPAF groups and a priori knowledge about the sizes of groups was estimated based on analyzing sample size and was used by GC&RT algorithm. The classification tree labeled CRT1 was designed (Fig. 1) and the classification properties of obtained model are presented in the Table 3. D. General CHAID model CHI-squared Automatic Interaction Detector (CHAID) represents one of the oldest algorithms for classification tree design. The CHAID classification trees do not have to be binary and in a single node can have more than two branches. Because of simplicity the CHAID design algorithm can be used for the analysis of very large date sets. The CHAID algorithm first divides a continuous predictor into a number of categories with about equal

TP cases FN cases TN cases FP cases sensitivity specificity positive predictivity negative predictivity accuracy

learning sample

testing sample

180 67 515 168 72,9% 75,4% 51,7% 88,5% 74,7%

65 47 238 81 58,0% 74,6% 44,5% 83,5% 70,3%

number of observations so it becomes a categorical predictor. For each predictor a pair of category that is least significantly different in Pearson Chi-square test, with respect to the dependent variable for the classification, is determined. The algorithm then makes a choice of the predictor variable that yields the most significant split. Terminal nodes are defined as the points were no more splits can be performed because p-value for selected predictor is greater than some pre-adjusted smallest value. The observations that have missing data in any of predictor variables are excluded from the analyses. Quality measures of the CHAID classification tree model labeled CHAID1 is presented in Table 4. E. Boosting Tree Classifiers Model Gradient Boosting Trees is a method that repeatedly applies predictive function in the series and weights the output of each function so that the total error of the prediction is minimized. The predictive accuracy of such a series greatly exceeds the accuracy of the base function used alone. After the first tree is designed, the residuals (error values) from the first tree are then fed into the second tree which attempts to reduce the error. This process is repeated through a chain of successive trees. The final predicted

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Classification Methods for Atrial Fibrillation Prediction after CABG

Table 5

Quality measures for Boosting Tree model labeled BOOST1.

TP cases FN cases TN cases FP cases sensitivity specificity positive predictivity negative predictivity accuracy

learning sample

testing sample

297 144 741 25 67,3% 96,7% 92,2% 83,7% 86,0%

91 38 272 26 70,5% 91,3% 77,8% 87,7% 85,0%

49

The predicted values for the dependent variable is never less than or equal to 0, or greater than or equal to 1, regardless of the values of the independent variables because of logit or logistic transformation [4]. After building the logit regression model (labeled LOGIT1) which quality measures are presented in Table 6, a test of significance for the predictors was performed and they are presented in order of the importance for the model: PonPoffAVR5, PonPoffAVR_RR, relPslope1Pslope2AVR5, Pslope2DEV5, Pslope2AVR5, PonQonAVR4 (for more the detailed description of noted predictors see Table 1). III. CONCLUSIONS

value is formed by adding the weighted contribution of each tree. Models designed as an additive series of trees are among the most accurate and they achieve better results than any other known modeling technique [4] and our results in Table 5 support that statement. The primary disadvantage of the Boosting Tree is that the model is complex and cannot be visualized like a C&RT or CHAID models. After building the boosting tree, predictor statistics can be calculated. The predictors are given on order of the importance: relPslope1Pslope2AVR5, WenergyAVR4, Pslope2AVR5, WenergyAVR5, RRAVR, Pslope2DEV5, PonQonAVR4, PonPoffAVR_RR (for more the detailed description of noted predictors see Table 1). F. Binomial Logit Regression Models Binomial Logit Regression Model estimates the relationship between more continuous independent variables (predictors) with the binary dependent variable which specifies the case belonging to the class. The cases that belong to the class of patients who have developed AF were coded with 1 and the cases that belong to the class of patients who have not developed AF were coded with 0. Table 6 Quality measures for Binomial Logit model labeled LOGIT1. TP cases FN cases TN cases FP cases sensitivity specificity positive predictivity negative predictivity accuracy

learing sample

testing sample

156 91 626 57 63,2% 91,7% 73,2% 87,3% 84,1%

55 57 293 28 49,1% 91,3% 66,3% 83,7% 80,4%

In our previous work we have found that several additional P wave ECG parameters may be relevant for early CABG AF prediction [1]. However, manipulating with a large number of parameters does not allow easy and simple decision making and demands formal blind studies. We have evaluated five classification tree models on our data samples. The Boosting Trees classifies tree showed best results, i.e. highest overall sensitivity and accuracy, which might had been expected due to the highest complexity. Our results are comparable with the results obtained in previous studies and we expect that inclusion of additional parameters from the patient history will improve the classification.

ACKNOWLEDGMENT This study was supported by Ministry of Science, Education and Sport of the Republic of Croatia under grant no. 036-0362979-1554.

REFERENCES 1. 2. 3. 4.

Sovilj S, Rajsman G, Magjarević R. Multiparameter Prediction Model for Atrial Fibrillation after CABG. Detector. Proc. Computers in Cardiology 2006; 33:489-92. Sovilj S, Rajsman G, Magjarevic R. Continuous Multiparameter Monitoring of P Wave Parameters after CABG Using Wavelet Detector. Proc. Computers in Cardiology 2005; 32:945-8. Poli S, Barbaro V, Bartolini P, Calcagnini G, Censi F. Prediction of AF from surface ECG: review of methods and algorithms. Ann 1st Super Sanita 2003; 39(2). StatSoft Inc. at http://www.statsoft.com Author: Siniša Sovilj Institute: Street: City: Country: Email:

University of Zagreb Faculty of Electrical Engineering Unska 3 Zagreb Croatia [email protected]

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Dynamic Repolarization Assessment and Arrhythmic Risk Stratification E. Pueyo1, M. Malik2 and P. Laguna1 1

2

Aragón Institute for Engineering Research (I3A), University of Zaragoza, Spain Department of Cardiological Sciences, St. George’s Hospital Medical School, London, UK

Abstract— A dynamic model is proposed to study the relationship between the QT and RR intervals of the surface electrocardiogram. The model accounts for the influence of a history of previous RR intervals on each QT, considering that such an influence may vary along the recording time. For identification of the model parameters, an adaptive methodology that uses the regularized Kalman filter is developed. A set of risk markers are derived from the estimated model parameters and they are tested on ambulatory recordings of postmyocardial infarction patients randomized to treatment with amiodarone or placebo. The results of our study show that amiodarone substantially modifies the QT interval response to heart rate changes. Furthermore, the way amiodarone acts on QT adaptation allows to identify patients in which treatment is being effective and separate them from those in which it is not and, consequently, are at higher risk of suffering from arrhythmic death. Keywords— Electrocardiogram, repolarization, QT/RR, arrhythmic death.

I. INTRODUCTION Numerous studies have pointed out the tight relationship that exists between the QT interval, which expresses the entire duration of ventricular depolarization plus repolarization, and the RR interval, which is the inverse of heart rate. It has been suggested that characteristics derived from such a relationship can be used to detect or predict states associated with high arrhythmic risk [1]. The use of long-term electrocardiographic (ECG) recordings is recommended for risk stratification studies based on repolarization analysis [2]. Those types of recordings contain sharp changes in heart rate and, consequently, QT hysteresis needs to be considered when exploring the QT/RR relationship. Hysteresis refers to the fact that the QT interval is not able to follow the RR interval changes instantaneously but there is a time lag in the adaptation. In [3] a method was developed to investigate QT changes after RR in ambulatory recordings of post-myocardial infarction (MI) patients of the EMIAT database that were followed-up during a mean time of two years. Using the proposed method, several indices characterizing QT/RR adaptation, including the time that QT needs to follow RR changes, were evaluated. Those indices showed strong ca-

pacity to discriminate between patients at low and high risk of arrhythmic death while on therapy with amiodarone. Although in [3] the time and profile of QT adaptation were considered to be specific of each patient, it was assumed that those characteristics did not vary along the recording time. In order to account for the dynamic properties of the QT/RR relationship, a time-variant methodology is used in the present paper that extends the one described in [3]. Based on that method we have investigated QT dependence on RR over the same recordings of the EMIAT database and we have derived new markers characterizing QT dynamicity. The potential of those markers for arrhythmic risk stratification is presented. II. METHODS A. Population and data measurements The study population comprises 939 patients of the EMIAT database. Patients were survivors of acute MI randomized to treatment with amiodarone or placebo. All of them were aged less than 75 years and had a left ventricular ejection fraction (LVEF) inferior to 40%. Meaningful clinical data were available in 866 patients who were followedup during a mean time of 620 days (±176). Of these patients, 404 received placebo and 462 were treated with amiodarone. There were 26 arrhythmic deaths in the placebo group and 18 in the amiodarone group. The electrocardiographic recordings were obtained one month after randomization. All of them were 24-hour Holter ECGs with 3 recorded leads. In each lead, individual QT and RR intervals were measured using the commercial software of Pathfinder 700 Holter system (Reynolds Medical, Hetford, UK). These measurements where checked by an expert and, where necessary, they were corrected manually or deleted. For each patient and lead, the number of cardiac cycles where it was possible to determine both the RR measurement and the QT measurement were counted. The lead that presented the largest number of accepted beats was selected for further analysis. Potential outliers in the RR and QT series were removed by applying a procedure based on a Median Absolute Deviation (MAD) filter. The clean series were interpolated linearly at a sampling fre-

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Dynamic Repolarization Assessment and Arrhythmic Risk Stratification

quency of 1 Hz and low-pass filtered (0.05 Hz) to avoid the sympathetic and parasympathetic influences of the Autonomic Nervous System. The final series are denoted by x RR (n) and y QT (n) , respectively.

The QT/RR relationship is modeled by considering a nonlinear system with memory that has x RR (n) as its input signal and y QT (n) as its output. The system is assumed to be composed of two blocks (see Fig. 1). The first block is a linear time-variant FIR filter of order N: h(n) = [ h 0 ( n ) … h N −1 (n) ]

T

∈ ℜ N×1

(1)

x RR (n) = [x RR (n) x RR (n − 1) … x RR (n - N + 1)] . (2) T

The second block is a time-varying nonlinearity represented by a first-order polynomial: g ( z RR (n) , a(n) ) = aT (n) z RR (n)

(3)

a(n) = [a 0 (n) a 1 (n)] ∈ ℜ 2×1

and

T

[

z RR (n) = 1 z RR (n)

]

∈ℜ

h(n)

g ( . , a(n))

y QT (n)

+

2×1

.The order of the linear filter is

defined as N=50 based on the results reported in [3] where it is shown that the initial 40 - 50 RR intervals previous to each QT are the most clinically relevant. The objective of our study is to identify the described system only from the knowledge of the input and output signals. In order to guarantee uniqueness in the determination of the filter weights and the polynomial coefficients, a normalization constraint on the weights is imposed: h T (n) ⋅ 1 = 1 , ∀ n, with 1 denoting the N × 1 vector of ones. Also, constraints referred to weights being positive are introduced so as to be able to derive physiologically plausible interpretations. With all the above mentioned constraints, the output of the first block can be interpreted as a weighted-averaged RR measurement (which is optimally defined at each time n), while the output of the second block expresses the evolution of the QT interval as a function of such an averaged RR measurement. Finally, the output of the global system is considered to be contaminated with some additive white noise v(n) that can include delineation errors and/or inaccuracies due to modeling assumptions: y QT (n) = a T (n) z RR (n) + v(n) .

C. System identification State-space formulation Denoting

θ(n) = [ a 0 (n) a1 (n) h 0 (n) … a1 (n) h N-1 (n) ] s(n)= [1 x RR (n) … x RR (n-N+1) ] , T

T

(5) (6)

the output y QT (n) can be expressed as

whose output is z RR (n) = h T (n) ⋅ x RR (n) , where

T

x RR (n)

v ( )

Fig. 1 Block diagram of the dynamic model proposed in this study.

B. Model Composition

with

75

(4)

y QT (n) = s T (n) θ(n) + v(n)

(7)

where s(n) is the known observation vector and θ(n) is the parameter vector to be estimated, both of dimension (N+1) × 1. In addition to the observation equation (7), a second equation describing the time-varying nature of the system state is incorporated: θ(n + 1) = θ(n) + w (n) .

(8)

The two equations (7) and (8) constitute a state-space representation of the system to be identified. In such a representation, the noises v(n) and w(n) are assumed to be uncorrelated zero-mean white processes with the variance of v(n) denoted by σ 2v (n) and the covariance of w(n) denoted by Qw(n). The initial state of the system, θ(0) , is assumed to be uncorrelated with v(n) and w(n). The mean μ0,θ of θ(0) is defined using equation (5) and vectors μ0,h and μ0,a initially defined employing simple fitting procedures applied to the initial samples of the data series. The covariance matrix Π0,θ of θ(0) is taken as the identity matrix. Kalman filter with regularization Estimation of the system vector θ(n) is performed utilizing the Kalman filter (KF). The Kalman Filter is a linear adaptive MMSE filter that is able to deal with nonstationary environments, which are the type of environments we encounter when analyzing the QT/RR relationship over ambulatory recordings. Direct application of KF to our formulated problem implies that, at each time n, N+1 parameters need to be estimated from a unique observation, as it can be observed from equation (7). In order to make the solution more robust

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against noise or imprecision that can be present at the output y QT (n) , regularization is incorporated into the problem.

This means that additional a priori information on the solution is added. This is performed in our study by augmenting the observation equation (7) as described next: y QT (n) = S T (n) θ(n) + v(n)

(9)

where ⎡ sT (n) ⎤ ⎡ y (n) ⎤ ⎡ v(n) ⎤ y QT (n) = ⎢ QT ⎥ , S(n) = ⎢ ⎥ , v(n) = ⎢ ⎥ (10) 0 D β(n) (n) ⎣ ⎦ ⎣ v'(n) ⎦ ⎣ ⎦

In equation (10), 0 denotes the N × 1 vector of zeros and β(n) is a scalar called regularization parameter, which is selected using the so-called L-curve criterion [4]. The matrix D(n) is defined so as to force the filter weights follow a relation close to an exponential one. That selection is based on our experience about QT dependence on RR [3] and, in any case, the strength put on that type of smoothing is determined along with the state estimation. The noise v'(n) is a fictitious zero-mean noise uncorrelated with θ(n) and v(n) and with covariance matrix taken as the identity. In the application of the KF, the variance σ 2v (n) of the measurement noise v(n) and the covariance Qw(n) of the process noise w(n) are estimated following the approach proposed in [5]. Constraints With the objective of having an estimate of θ(n) satisfying the constraints described in section II.B, the constraint space Ω is built and the unconstrained solution θˆ (n) is projected onto it. Ω is defined by the condition that all of the elements of the estimated state vector, except the first one, have the same sign. This guarantees that the estimated weights are positive. Once the projection ˆ θˆ (n) = arg min

θ (n)∈Ω

{ (θ(n) − θˆ (n)) (θ(n) − θˆ (n)) } T

(12)

is obtained, the constrained solution is renamed as θˆ (n) . Estimates hˆ (n) and aˆ (n) are readily derived from θˆ (n) . D. Clinical markers

A number of indices are proposed for clinical comparisons. Some of those indices are defined from the variable L90(n) that measures the time required by the QT interval to complete 90% of its adaptation in response to RR changes. The variable L90(n) is calculated at each instant n using the

weight profile hˆ (n) estimated in II.C [5]. The proposed indices are L90,acc , L90,dec and L90,sta , which are defined as the mean of L90(n) in response to heart rate accelerations, decelerations and stable rate periods, respectively. In each recording, those types of periods are identified following the approach proposed in [5]. Other indices are defined from the slope of the line that fits the ⎡⎣ yQT (n) , z RR (n) ⎤⎦ data in small neighborhoods around each value of z RR (n 0 ) . The proposed markers are sacc, sdec and ssta, which are calculated as the mean of the estimated aˆ 1 (n) in periods of accelerating, decelerating and stable rate, respectively.

III. RESULTS AND DISCUSSION A. Dynamic QT adaptation

Evaluation of QT lag behind RR changes revealed substantial differences along the 24-hour recording. In mean over recordings, the time required by the QT interval to complete 90% of the adaptation was L90,dec = 2.1 min when measured after a heart rate deceleration, L90,ace = 1.6 min after a rate acceleration, and L90,sta = 1.9 min under stable rate conditions. The slope s described in section II.D was as well evaluated in episodes of decelerating, accelerating and stable heart rate. The mean slope values were sdec = 0.152, sacc = 0.135 and ssta = 0.127, respectively. The results obtained in our study corroborate the hypothesis that QT dependence on RR is not constant along the recording time but such a dependence changes in response to heart rate variations. Specifically, we found that QT adaptation after a sudden rate acceleration is more rapid than after a stable or a decelerating rate period. This can be explained by the fact that readjustment of cell mechanisms needs to be completed faster after a heart rate acceleration so as to avoid beat overlapping. B. Clinical risk stratification

The risk markers described in section II.D were separately assessed in the placebo and amiodarone arms. In each of the two arms, independent analysis was performed for the group of patients who suffered arrhythmic death while on therapy and the group of those who survived. Results are presented in Table I. It can be observed that survivors treated with amiodarone have prolonged QT adaptation times as compared to those treated with placebo, either when the adaptation time is measured in accelerating (L90,acc), decelerating (L90,dec) or stable (L90,sta) rate periods.

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Dynamic Repolarization Assessment and Arrhythmic Risk Stratification

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Table 1 Mean and standard error of the mean for the markers described in II.D. Units are: min for L90,acc , L90,dec and L90,sta ; n.u. for sacc , sdec and ssta Placebo

Amiodarone

L90,acc L90,dec L90,sta

Survivors Mean ± SEM 67,89 ± 2,77 99,25 ± 4,18 88,76 ± 6,08

Victims Mean ± SEM 110,55 ± 27,04 153,50 ± 28,61 190,59 ± 73,91

sacc sdec ssta

0,059 ± 0,003 0,085 ± 0,003 0,066 ± 0,004

0,091 ± 0,024 0,122 ± 0,025 0,114 ± 0,029

p-value 0,133 0,075 0,186

Survivors Mean ± SEM 112,31 ± 6,43 146,35 ± 7,98 134,36 ± 9,08

Victims Mean ± SEM 75,68 ± 7,21 111,93 ± 14,34 115,42 ± 32,35

p-value 4 · 10-4 0,047 0,581

0,200 0,159 0,120

0,084 ± 0,005 0,113 ± 0,005 0,095 ± 0,006

0,045 ± 0,011 0,064 ± 0,011 0,063 ± 0,014

0,006 0,001 0,049

On the other hand, victims on amiodarone show reduced adaptation times with respect to values found in the placebo arm. Similar observations can be made using the variables that measure the slope of the QT/ RR relationship: in amiodarone, survivors exhibit increased slope values, while victims have reduced values. These results confirm our previous observations that amiodarone modifies QT adaptation and such a modification is different for victims and survivors of arrhythmic death [3]. The advantage of the dynamic method presented in this study is that local repolarization heterogeneities can be effectively detected even if they occur only at isolated episodes of the recording where heart rate experiments sudden changes. On the contrary, with the assumption that QT adaptation preserves constant characteristics in one and the same patient, local heterogeneities can be masked if, on average, the adaptation process is not severely altered. Arrhythmic death has been usually associated with rate exertion [6]. Our results suggest that amiodarone improves repolarization adaptation by delaying the response of the QT interval to rate accelerations. Those patients, in which amiodarone is not able to provoke such a delay are at higher risk of suffering from arrhythmic death. In a similar manner, when a heart rate deceleration occurs, amiodarone increases the QT adaptation time so as to prevent excessive increased QT lengthening in the early phase of rate decelerations, which could trigger ventricular arrhythmias [7]. Patients, in which amiodarone is not being effective show shorter adaptation times, indicating higher vulnerability to arrhythmic death.

ACKNOWLEDGMENT This study was supported by Ministerio de Ciencia y Tecnología and FEDER under Project TEC2004-05263-

C02-02, in part by the Diputación General de Aragón (DGA), Spain, through Grupos Consolidados GTC ref:T30, and by CIBER through ISCIII CB06/01/0062.

REFERENCES 1.

2. 3.

4. 5.

6. 7.

Chevalier P, Burri H, Adeleine, Kirkorian G, Lopez M, Leizorovicz A, Andre-Fouet X, Chapon P, Rubel P, Touboul P (2003) QT dynamicity and sudden death after myocardial infarction: results of a long-term follow-up study. J Cardiovasc Electrophysiol, 14(3):227– 233 Lux R, Kirchhof P, Cygankiewcz I, Brockmeier K (2007) Electrocardiographic markers of sudden cardiac death. J Electrocardiol 40(1 Suppl):S9–S10 Pueyo E, Smetana P, Caminal P, Bayés de Luna A, Malik M, Laguna P (2004) Characterization of QT interval adaptation to RR interval changes and its use as a risk-stratifier of arrhythmic mortality in amiodarone-treated survivors of acute myocardial infarction. IEEE Trans Biomed Eng 51(9):1511–1520 Golub GH, Hansen PC, O´Leary DP (1999) Tikhonov regularization and total least squares. SIAM J Matrix Annal Appl 21(1):185-194 Pueyo E (2006) Detección de heterogeneidades en la depolarización y repolarización cardiacas a partir del electrocardiograma como mejora en la predicción del riesgo frente a arritmias. PhD dissertation. University of Zaragoza, Spain, 2006 Cobb LA, Weaver WD (1986) Exercise: a risk for sudden death in patients with coronary heart disease. J Am Coll Cardiol 7(1):215–219, Review Singh JP, Johnston J, Sleight P, Bird R, Ryder K, Hart G (1997) Left ventricular hypertrophy in hypertensive patients is associated with abnormal rate adaptation of QT interval. J Am Coll Cardiol 29(4):778–784

Esther Pueyo Aragon Institute for Engineering Research (I3A), University of Zaragoza C/ Maria de Luna, 1 Zaragoza Spain Email: [email protected]

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Effects of vagal blockade on the complexity of heart rate variability in rats M. Baumert1, E. Nalivaiko2 and D. Abbott1 1

Centre for Biomedical Engineering, School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, Australia; 2 Department of Human Physiology and Centre for Neuroscience, Flinders University, Adelaide, Australia.

Abstract— In this paper we investigate the influence of vagal blockage on heart rate variability complexity measures. Nine conscious rats are injected with methyl-scopolamine brobide (50 µg/kg s.c.). We analyze 10 minute segments of beat-to-beat intervals before and after injection by standard time and frequency domain methods, compression entropy, sample entropy, Poincaré plot, detrended fluctuation analysis and symbolic dynamics. All parameter domains show changes in heart rate variability after vagal blockade, indicating a decrease in heart rate complexity. In conclusion, vagal modulation plays an important role in the generation of heart complexity in rats or, in other words, heart rate complexity measures are sensitive to vagal heart rate modulation.

dance with the European Community Council Directive of 24 November 1986 (86/609/EEC), and are approved by the Flinders University Animal Welfare Committee. During preliminary surgery, telemetric ECG transmitters (TA11CA-F40, Data Science International, USA) are implanted into the peritoneal cavity under isoflurane (1.5% in 100% oxygen) anesthesia. On the day of experiment, ECG is recorded before and after administration of methylscopolamine bromide (50 µg/kg s.c., Sigma, USA), a vagal blocker that does not cross blood-brain barrier. Analogue signal is acquired using the MacLab interface and Chart software (ADInstruments, Sydney, Australia).

Keywords— heart rate variability, complexity, vagal blockade, rat

B. Heart rate variability analysis

The heart rate underlies beat-to-beat variations, reflecting modulations mediated by vagal and sympathetic branches of the autonomic nervous system. Heart rate variability (HRV) analysis has shown prognostic significance in patients after acute myocardial infarction [1] and in the diagnosis of autonomic neuropathy [2]. Furthermore, it is used in various research settings such as sports [3] or obstetrics [4]. The quantification of HRV is basically a time series analysis task, and numerous approaches have been proposed, including traditional time and frequency domain measures [5], but also measures from complex systems science [4,6,7,8]. Although the sensitivity of some of those new measures often appeared superior to standard time and frequency domain measures, their physiological meaning is hardly understood and their interpretation remains difficult. To assess their sensitivity to vagal heart rate modulation we investigate the impact of vagal blockade on HRV complexity measures in a rat model.

Pre-processing: RR intervals series are extracted from the ECG recording, using the Chart® software (ADInstruments, Sydney, Australia). Subsequently, RR time series are scanned and manually edited. Artifacts and ectopic beats are filtered, resulting in a normal-to-normal (NN) interval time series. For further analysis we select a segment of a 10 minute length, starting 15 minutes prior to the vagal blockade injection in order to obtain HRV baseline values. To analyze HRV during vagal blockade, we select ten minute epochs, beginning five minutes after injection. 220 injection

200 NN interval [ms]

I. INTRODUCTION

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Fig. 1 Beat-to-beat interval time series in a conscious rat prior and after injection of methyl-scopolamine. The NN interval as well as heart rate variability deceases.

The study is performed on nine male Wistar Hooded rats weighing 250-300 g. Experiments are conducted in accor-

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Effects of vagal blockade on the complexity of heart rate variability in rats

Time domain analysis: For traditional time domain analysis of HRV we compute meanNN, the mean beat-tobeat interval of normal heart beats, its standard deviation sdNN, and the root-mean-square of successive beat-to-beat differences rmssd. Frequency domain analysis: For frequency domain analysis of HRV we generate equidistant time series, using a linear interpolation at 20 Hz. Subsequently, the power spectrum is estimated, using FFT and a Blackman-Harris window. Total power (P: 0-3Hz), very low frequency power (VLF: 0.03-0.25 Hz), low frequency power (LF: 0.25-1 Hz) as well as high frequency power (HF: 1-3 Hz) are computed. Poincaré plots: Poincaré plots provide a visual way to study dynamics underlying HRV. As commonly used, NN intervals are plotted against the previous ones (i.e. NNn+1 vs. NNn). Although this approach is somewhat simplified with regard to the non-linear systems theoretical intention, it is a useful tool for HRV analysis. Usually, an ellipsoid shape is fitted to the points and the short axis SD1 and long axis SD2 are taken as measures. Although the Poincaré plot itself may capture non-linear characteristics of HRV, SD1 as well as SD2 capture only linear characteristics [9]. Compression entropy: To study the short-term complexity of beat-to-beat fluctuations we recently introduced a compression based complexity measure [8]. From the point of information theory, the smallest algorithm that produces a string is the entropy of that string (Chaitin-Kolmogorov entropy). Although it is theoretically impossible to develop such an algorithm, data compression techniques might provide a good approximation. We apply a modified version of the LZ77 algorithm for lossless data compression introduced by Lempel and Ziff in 1977 [10]. The algorithm is based on a sliding window technique and searches for matching sequences. It keeps the w, the most recently encoded source symbols (sliding window of size w). The notyet-encoded sequence of symbols is stored in the lookahead buffer of size b. The encoder positioned at p looks for the longest match of length n between the not-yet-encoded p + n -1

n-string x p

in the look-ahead buffer and the already p − w + v + n −1

encoded string x p − w + v

in the window beginning at

position v. Thus, the matching string of n symbols is simply encoded by encoding the integer numbers n and v, i.e. a pointer to the previous occurrence of this string in the sliding window. Then the position and length of the matching sequence are stored. The ratio of the uncompressed to the compressed file, called compression entropy Hc , is used as complexity measure. We set b = 3 and w = 7 as previously published [8].

27

Symbolic Dynamics: The concept of symbolic dynamics allows a simplified description of the dynamics of a system with a limited amount of symbols. Methods based on symbolic dynamics have already been successfully applied to HRV analysis providing some more global information about the underlying system. In this study we employ the technique proposed by Voss et al. [6]. The difference between each NN interval and mean NN is transformed into an alphabet of 4 symbols {0, 1, 2, 3}. Symbols ‘0’ and’2’ reflect low deviation (decrease or increase) from the mean NN interval, whereas ‘1’ and ‘3’ reflect a stronger deviation (decrease or increase over a predefined limit). Subsequently, the symbol string is transformed to words (bins) of three successive symbols. The distribution of word types reflects some nonlinear properties of HRV (see [6] for detailed information). From this symbolic dynamics the following parameters are calculated: WPSUM13: words that contain only symbols ‘1’ and ‘3’ reflecting high variability; WPSUM02: words that contain only symbols ‘1’ and ‘3’ reflecting high variability; FORBWORD: number of word types that occur seldom, i.e. with a probability less 0.001. Using a modified symbol transformation consecutive NN differences less than 2 ms are coded as ‘0’ and otherwise as ‘1’. In this way two further parameters are obtained: PLVAR2: percentage of words of length 6 that contain only ‘0’, reflecting a low variability; PHVAR2: percentage of words of length 6 that contain only ‘1’, reflecting a high variability. Sample entropy: Sample entropy (SampEn) calculates the probability that epochs of window length m that are similar within a tolerance r remain similar at the next point. SampEn is precisely the negative natural logarithm of the conditional probability that a dataset of length N, having repeated itself within a tolerance r for m points, will also repeat itself for m + 1 points, without allowing self-matches. In agreement with previously published studies we choose values of r = 0.25 and m = 2 [4]. Detrended fluctuation analysis: The DFA technique has been developed to analyze long-range correlations (longmemory dependence) in non-stationary data, where conventional fluctuation analyses such as power spectra and Hurst analysis cannot be reliably used [7]. The method works as follows: 1. Comput1.e the cumulative sum c(k ) = ∑k [s(k ) − s ] of i =1 the time series s where s is the mean of S (using the concept of Random-Walk-Analysis). 2. Compute the local trend cn (k ) within boxes of varying sizes n (least square fit). 3. Compute the root mean square of the detrended time series in dependency on box size n as

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M. Baumert, E. Nalivaiko and D. Abbott

F ( n) =

1 N 2 ∑ [c( k ) − cn ( k )] , where N denotes the size N k =1

of S. 4. Plot log10 F(n) against log10 n. If the data displays long-range dependence then F(n) ~ nα, where α is the scaling exponent. For stationary data with scale-invariant temporal organization, the Fourier power spectrum S(f) is S(f) ~ f- β, where the scaling exponent β is related to α in the following way: β = 2α-1. Values of 0 < α < 0.5 are associated with anti-correlation (i.e. large and small values of the time series are likely to alternate). For Gaussian white noise α = 0.5. Values of 0.5 < α ≤ 1 indicate long-range power-law correlations (i.e. large values of the time series are likely to be followed by large values). Values 1 < α ≤ 1.5 represent stronger long-range correlations that are different from power-law where α = 1.5 for Brownian motion. We compute two scaling exponents, αLF and αVLF that are related the LF and VLF frequency ranges as defined above. In order to estimate frequency values fn from the segment size n of DFA, in Hertz, the segment sizes are related to the mean heart rate (meanNN-1), i.e. fn ≈ meanNN-1n [11]. Table 1 Heart rate variability measured in nine rats before (baseline) and after injection of methyl-scoplamine (vagal blockade) displayed as medians and inter-quartile ranges as well as p-values of the Wilcoxon test for paired comparisons of medians HRV measure baseline

vagal blockade

p

meanNN sdNN Rmssd P VLF LF HF Hc SD1 SD2 WPSUM13 WPSUM20 FORBWORD PLVAR2 PHVAR2 SampEn αLF αVLF

144 [131 – 155] 4.3 [4.0 – 5.0] 0.8 [0.8 – 1.0] 13.6 [8.6 – 21.7] 1.7 [1.1 – 2.3] 0.11 [0.10 – 0.18] 0.22 [0.21 – 0.32] 0.31 [0.30 – 0.31] 0.6 [0.5 – 0.7] 6.1 [5.6 – 7.1] 0.06 [0.04 – 0.18] 0.92 [0.75 – 0.92] 42 [42 – 44] 0.94 [0.87 – 0.96] 0.000 [0.000 – 0.000] 0.42 [0.33 – 0.73] 1.01 [0.98 – 1.18] 1.41 [1.38 – 1.44]

0.0039 0.098 0.0039 0.30 0.0039 0.0039 0.0039 0.0039 0.0039 0.098 0.57 0.30 1 0.0039 0.0039 0.074 0.16 0.0039

179 [178 – 198] 7.6 [5.0 – 10.1] 2.5 [2.1 – 2.9] 33.9 [15.8 – 45] 7.2 [6.2 – 11.9] 1.54 [0.74 – 2.33] 1.63 [0.90 – 2.33] 0.41 [0.39 – 0.43] 1.8 [1.5 – 2.1] 10.6 [7.0 – 14.3] 0.11 [0.04 – 0.24] 0.73 [0.58 – 0.94] 42 [42 – 42] 0.15 [0.04 – 0.17] 0.016 [0.003 – 0.024] 1.00 [0.84 – 1.19] 0.96 [0.85 – 1.04] 1.31 [1.22 – 1.34]

III. RESULTS After injection of methyl-scopolamine, a vagal blocker, the heart rate increases, i.e. meanNN reduces in all rats. Fig.1 shows an example of the NN interval time series before and after injection. For statistical comparison of HRV measures we compute group medians, inter-quartile ranges as well as the Wilcoxon test. Results are summarized in Tab.1. The overall HRV, as measured in the time domain via sdNN, is reduced in trend, but does not reach statistical significance. A more detailed analysis of HRV reveal drastically reduced beat-to-beat variability (rmssd). The frequency domain analysis shows that the high (HF), low frequency (LF), and very low frequency (VLF) power is reduced, whereas the overall power is decreased in trend only. The Poincaré plot based analysis shows significantly reduced short-term fluctuations (SD1) and nearly significant reduction in SD2. The compression entropy is also significantly reduced after vagal blockade. Symbolic dynamics based analysis reveals an increase of low variability patterns (PLVAR2) paralleled by a decrease in high variability patterns (PHVAR2), whereas the other parameters are not significantly changed. The sampling entropy is reduced after vagal blockade, but does not reach statistical significance. Scaling analysis by means of DFA shows increased correlations after vagal blockade in the VLF range. IV. DISCUSSION In this paper we study HRV, particularly its complexity measures, and changes in these measures caused by vagal blockade in conscious rats. It is well known that vagal blockade leads to an increase in heart rate paralleled by a decrease in variability in the high frequency range [12]. Little is known, however, about the effect of vagal blockade on the complexity of HRV or, in other words, about the sensitivity of complexity measures to vagal modulation of the heart rate. Our data shows the typical increase in heart rate paralleled by a decrease in HRV after vagal blockade. Besides the decrease in HF and rmssd we find LF oscillations to be almost vanished, suggesting that without external stress, LF fluctuations in heart rate are almost completely caused by vagal mechanisms. But also the VLF oscillations are significantly reduced during vagal blockade. This broad reduction in HRV also affects most of the complexity measures, showing a decreased complexity. The compression based measure Hc assesses fluctuations of

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Effects of vagal blockade on the complexity of heart rate variability in rats

heart rate within a short time window and is therefore sensitive to vagal modulations. Looking at the Poincaré plot, SD1 reflects beat-to-beat changes that are exclusively mediated by the vagal pathway whereas, SD2 assesses also slower dynamics that might be caused by other regulatory systems, external stimuli or animal’s movements, and consequently shows less sensitivity to vagal modulations. The contradictory behavior of the Symbolic Dynamics measures PLVAR2 and PHVAR2 that assess beat-to-beat dynamics over 7 consecutive heart beats also reflect the low HRV after vagal blockade. In particular there are no more heart rate patterns with consecutive beat-to-beat changes higher than 2 ms (PHVAR2). Sampling entropy is less sensitive to vagal blockade since this regularity statistic is based on the overall variability of the NN time series that is also influenced by slow trends that are not caused by vagal modulations. The DFA shows a steeper slope in the VLF range and therefore increased long-range correlations after vagal blockage, which also suggest that vagal modulations cause a certain amount of irregularity in HRV and consequently a vagal blockade is leading to a more regular behavior at larger scales. Given that heart rate modulations mediated by vagal efferents mainly reflect cardio-respiratory coupling it could be speculated that the irregularity of respiration, including factors such as respiratory frequency, tidal volume, ratio between inspiration and respiration, etc. is the major source of the complexity found and assessed with the above described measures. This emphasizes the necessity of recording respiration in HRV analysis studies.

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REFERENCES 1.

Kleiger RE, Miller JP, Bigger JT et al. (1987) Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 59:256–262 2. Vinik AI, Maser RE, Mitchel BD et al. (2003) Diabetic autonomic neuropathy. Diabetes Care 26:1553-1579 3. Baumert M, Brechtel L, Lock J et al (2006) Heart Rate Variability, Blood Pressure Variability, and Baroreflex Sensitivity in Overtrained Athletes. Clin J Sport Med 16:412–417 4. Lake DE, Richman JS, Griffin MP et al. (2002) Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Inter Comp Physiol 283:R789-R797 5. Task Force of the European Society for Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. 17:354-381 6. Voss A, Kurths J, Kleiner HJ et al. (1996) The application of methods of nonlinear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. 31:419-433 7. Peng CK, Havlin S, Stanley HE et al (1996) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82-87 8. Baumert M, Baier V, Haueisen J et al. (2004) Forecasting of Life Threatening Arrhythmias Using the Compression Entropy of Heart Rate. Methods Inf Med 43:202-206 9. Brennan M, Palaniswami M, Kamen P (2001) Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng 48:1342-1347 10. Ziv J, Lempel A (1977) A universal algorithm for sequential data compression. IEEE Trans Inf Theory 23:337-343 11. Baumert M, Brechtel LM, Lock J et al. (2006) Scaling graphs of heart rate time series in athletes demonstrating the VLF, LF and HF regions. Physiol Meas 27:N35-N39 12. Japundzic N, Grichois ML, Zitoun P et al. (1991) Spectral analysis of blood pressure and heart rate in conscious rats: effects of autonomic blockers. J Auton Nerv Syst 30:91-100 Author: Dr Mathias Baumert

V.CONCLUSIONS Vagal blockade of heart rate control in rats shows the typical increase in heart rate paralleled by a decrease in HRV. Several complexity measures are decreased and are therefore sensitive to vagal heart rate modulation.

Institute: School of Electrical and Electronic Engineering, The University of Adelaide City: Adelaide, SA5005 Country: Australia Email: [email protected]

ACKNOWLEDGMENT This study was supported by grants form the Australian Research Council (DP0663345).

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Feature extraction and selection algorithms in biomedical data classifiers based on time-frequency and principle component analysis. P. S. Kostka1, E. J. Tkacz1 1

Institute of Electronics, Div. of Microelectronics and Biotechnology, Silesian University of Technology, Gliwice, Poland

Abstract— Proposed methods for feature extraction and selection stages of biomedical pattern recognition system are presented. Time-Frequency signal analysis based on adaptive wavelet transform and Principle Component Algorithm (PCA) algorithm is used for extracting and selecting from original data the input features that are most predictive for a given outcome. From the discrete fast wavelet transform coefficients optimal feature set based on energy and entropy of wavelet components is created. Then PCA is used to shrink this feature group by creating the most representative parameter subset for given problem, which is the input for last neural classifier stage. System was positively verified on the set of clinically classified ECG signals for control and atrial fibrillation (AF) disease patients taken from MITBIH data base. The measures of specificity and sensitivity computed for the set of 20 AF and 20 patients from control group divided into learning and verifying subsets were used to evaluate presented pattern recognition structure. Different types of wavelet basic function for feature extraction stage as well as supervised (Multilayer Perceptron) and unsupervised (Self Organizating Maps) neural network classification units were tested to find the best system structure.

ables is not always significant. This problem may undermine the success of machine learning that is strongly affected by data quality: redundant, noisy or unreliable information may impair the learning process. Proposed feature extraction tools almost always must depend on the specificity of classification task to be sensitive to features, which will be able to distinguish between health and pathology cases. The application field of presented multi-domain feature extraction and selection is the trial of

Keywords— feature extraction, feature selection, principal component analysis, pattern recognition, wavelet transform.

I. INTRODUCTION Pattern recognitions system structure (fig.1) after preliminary data preparation parts consists of two major stages [1]: • Feature extraction and selection • Classification The pattern to be recognized is first converted to some features, believed to carry the class identity of the pattern, and then the set of features is classified as one of the possible classes. To achieve high recognition accuracy, the feature extractor is required to discover salient characteristics suited for classification and the classifier is required to set class boundaries accurately in the feature space [1]. Progress made in sensor technology and data management allow researchers to gather data sets of ever increasing sizes, particularly with respect to the number of variables. However, the incremental informative content of such vari-

Fig.1

Pattern recognition system structure.

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Feature extraction and selection algorithms in biomedical data classifiers based on time-frequency and principle component analysis.

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detection of atrial fibrillation (AF), which is a supraventricular tachyarrhythmia characterized by uncoordinated atrial activation with consequent deterioration of atrial mechanical function. The purpose of feature or variable selection is to eliminate irrelevant variables to enhance the generalization performance of a given learning algorithm. The selection of relevant variables may also be useful to gain some insight about the concept to be learned. Other advantages of feature selection include cost reduction of data gathering and storage and computational speedup [2]. In this paper we investigate the efficiency of criteria derived from support vector machines (SVMs) for variable selection in application to classification problems. II. METHODS A. Feature extraction Before selection of the most representative features (II.B) the set of parameters characteristic for Atrial Fibrillation (AF) detection were composed of the time, frequency and mixed time-frequency domain (fig.2) [3]. This multidomain feature set covers a wide spectrum of possible AF activity occurrence. 1. Time domain: Duration time of atrial activation (P wale time) (tP) 2. Frequency domain: Frequency of oscillations after ventricular activity cancellation (FAF) 3. Mixed T-F domain: T-F analysis carried out by fast Mallat wavelet decomposion was used to compute following parameters based on energy and entropy, which correspond to the measure of information included in every frequency sub-band of Mallat jth decomposition (fig.3) [4],[5],[6]:

0 -0.05

0

500

1000

1500

2000

2500

Fig. 3 Multilevel Mallat decomposition components (details: d3-d7 with original ECG signal) of II ECG lead of patient with AF (fig.1).

• Energy of wavelet component

E1, j {ci , j } = (ci , j ) 2 ⇒ E11, j {s (n)} = ∑ (ci , j ) 2 i

• The (non-normalized) Shannon entropy:

E2, j {ci , j } = −(ci , j ) 2 log(ci , j ) 2 ⇒ E2, j {s (n)} = −∑ ⎡⎣(ci , j ) 2 log(ci , j ) 2 ⎤⎦ i

Full list of proposed T-F parameters are included in [7]. B. Feature selection There are two approaches to feature selection problem: I. Feature subset selection II. Feature projection, which tries to find optimal original feature combination (projection into new domain) into smaller set of new features. Principle component analysis (PCA) [8] or projection pursuit [9] are often used feature projection methods [10]. In presented algorithm, the most relevant features were obtained as the arbitrary number of the most principle components computed for multi-domain features (from point II.A), characteristic for AF detection. Principle Component Analysis: PCA realizates linear input data mapping (M-dimension space) into new feature space (L-dimension). In pattern recognition tasks it enables

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P. S. Kostka, E. J. Tkacz

to eliminate the uncorrelated noise and linear dependences in data.

[

]

T

= v1( j ) , v2( j ) ,..., vM( j ) , where: v ( j ) ∈ V ⊂ ℜ M , taken from dataset Γ = v (1) ,..., v ( P ) Every feature vector v

( j)

[

]

consisted of P feature vectors is mapped into reduced, L-

[

]

T

z ( j ) = z1( j ) , z2( j ) ,..., z L( j ) , ( j) L where z ∈ Z ⊂ ℜ ; L < M to fulfill MMSE minidimensional feature vector

malization criteria.

( j)

According to [11] each vector v can be approximated by the following sum with reduced dimension: L

v~ ( j ) = ∑ zi( j )ui + i =1

M

∑b u

i = L +1

i i

To chose the basis ortonormal vectors ui and set of coefficients bi to achieve the best, optimal approximation for every feature vector the sum of squared errors over the whole data set is created:

EL =

1 P ( j) ~( j) ∑ v −v 2 j =1

2

=

(

1 P M ( j) ∑ ∑ zi − bi 2 j =1 i=L+1

)

2

After some modifications [11] the minimum of the measure E L is achieved for the following form of basis vectors:

∑v ui = λiui where:

∑

v

is covariance matrix of the learning set

of v feature vectors ; ui are the eigenvectors and eigenvalues of

∑

v

λi are

the

.

PCA with mentioned above theoretical background was practically carried out according to algorithm presented in fig.4. Neural classifier structure of new selected feature vector F2 is the last stage of pattern classifier (fig.1), realized by: • Supervised learnt multilayer perceptron (MLP). • Unsupervised structure of Kohonen maps (SOMs).

Fig.4

PCA algorithm structure.

III. RESULTS To verify presented method, ECG signals taken from MITBIH database containing AF episodes were tested. Whole data set consisting of 40 cases with long term ECG recordings were divided into learning and verifying set. Performance of presented pattern recognition system was evaluated based on classical measures of classifier Sensitivity and Specificity. First group of tests (Table 1) was carried out for different types of basic wavelet structure on feature extraction stage (T-F analysis) and both supervised (Multilayer Perceptron) and nonsupervised (Self Organizating Maps) learnt neural structure used for final feature vector classification.

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Feature extraction and selection algorithms in biomedical data classifiers based on time-frequency and principle component analysis.

Classifier structures comparison 100

[%]

90 80 70 60 50 SOM DM

MLP DM

SOM PCA

MLP PCA

Type of classifier structure Sensitivity

Specificity

Fig. 5 Results of comparison the feature selection stage realized with the method based on PCA and different approach using with feature Discriminity Measure [3], which expresses what is the value of given feature separability.

value of classifier sensitivity S=92%, while specificity SP=90% for AF with different degree of organization (atrial flutter, AF1, AF2 and AF3). PCA used in feature selection stage gave better results then the other type of feature selection based on particular feature Discriminity Measure. To conclude, obtained results showed, that before pattern classifier can be properly designed and effectively used, it is necessary to consider the feature extraction and data reduction problems. Feature extraction should consists in choosing those features, which are most effective for preserving the class separability. Principal Component Analysis appeared as an effective tool for most representative features selection, improving whole classification process. Presented classification procedure gave satisfactory results, considering described classification algorithm as a contribution to atrial fibrillation arrhythmia detection on preliminary screen examination stage.

Table 1 Comparison of AF detection results for two structures of neural

REFERENCES

classifier part: multilayer perceptron (MLP) & Kohonen self organizating maps (SOMs), two types of feature extraction basic wavelet function. Neural Network classifier part type MLP + Feature extraction using db5 wavelet + PCA SOMs + Feature extraction using db5 wavelet +PCA MLP + Feature extraction using bior2.1 wavelet+PCA SOMs + Feature extraction using bior2.1 wavelet +PCA MLP class. NO preliminary feature extr. and sel. stage SOMs class. NO preliminary feature extr. and sel. stage

Sensitivity [%]

Specificity [%]

92

90

81

80

90

86

80

80

65

60

70

71

Second group of verifying tests (fig.5) was connected with the comparison of using PCA in feature selection stage with presented in our previous works [3] parameter selection algorithms based on the computing of ith feature Discriminity Measure, which expresses what is the value of its separability. IV. CONCLUSIONS After feature extraction from different time (T), frequency (F) and T-F domains, Principal Component Analysis was used to transform extracted features into new space with reduced size. Presented article focuses on PCA used for revealing of the features with maximal weight in classification process. It allowed to find the optimal feature subset selection of from different domain T-F features. Attrial Fibrillation detector tests gave for the optimal structure the

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1.

Duda R.O., Hart P.E. (1973) Pattern classification and scene analysis. John Wiley & Sons, New York. 2. Rakotomamonjy A. (2003), Variable Selection Using SVM-based criteria, Journal of Machine Learning Research 3:1357-1370. 3. Kostka P.S., Tkacz E.J. (2006), Hybrid Feature Vector Creation for Atrial Fibrillation Detection Improvement, Proc. of World Congress of Medical Physics and Biomedical Engineering, Seoul. 4. Mallat S. (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on Pattern An, 7(11):674-693 5. Akay M. Time-Frequency and wavelet analysis. IEEE EMB Magazine 14(2). 6. Thakor N.V. Sherman D.L. (1994) Biomedical problems in timefrequency-scale analysis – new challenges, Proceedings of the IEEESP pp. 536–539, 1994. 7. Kostka P.S., Tkacz E.J. (2005), Feature extraction optimization in neural classifier of heart rate variability signals, Proceedings of the 4th International Conference on Computer Recognition Systems CORES 2005, Advances in Soft Computing, Springer-Verlag, pp.585-594. 8. Karhunen, Joutsensalo (1995), Generalizations of principle component analysis, optimization problems, and neural networks, Neural Networks, vol. 8, No. 4, pp. 549-562. 9. Friedman, W.J. Tukey, J.W. (1974) A proj. pursuit alg. for exploratory data analysis, IEEE Trans. on Com., Vol. 23, pp. 881-889. 10. Fukunaga K. (1990), Introduction to statistical pattern recognition. 2nd edition. Academic Press, San Diego, CA. 11. Bishop C.M, (1996) Neural Networks for Pattern Recognition, Oxford University Press, New York. Corresponding author: Author: Pawel Kostka Institute: Street: City: Country: Email:

Institute of Electronics, Silesian University of Technology Akademicka 16 Gliwice Poland [email protected]

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Flexible Multichannel System for Bioelectrical Fields Analysis P. Kneppo1, M. Tysler2, K. Hana1, P. Smrcka1, V. Rosik2, S. Karas2, E. Heblakova2 1

Czech Technical University in Prague, Faculty of Biomedical Engineering, Kladno, Czech Republic 2 Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia

Abstract— Powerful multichannel system for measurement of surface biosignals is introduced and its use for cardiac electric field mapping is presented. The system can use up to 128 active electrodes; active neutralization of the patient and battery-powered operation facilitates optimal signal quality. Amplification and measuring unit containing ADSP-BF537 processor with embedded network connectivity enables easy wired or wireless connection to the host personal computer. Measurement and data analysis PC software based on the .NET platform enables fast high-resolution multichannel measurement, processing and visualization of surface bioelectrical fields. Another application enables noninvasive identification of local ischemia using measured QRST integral maps and model of the patient’s torso. Keywords— DSP based measuring system, Ethernet connectivity, biosignal measurement, body surface potential mapping, noninvasive ischemia identification

I. INTRODUCTION Multichannel measurement of surface bioelectric potentials and computation of body surface potential (BSP) maps is a noninvasive procedure used in cardiac or brain investigations that enables detailed analysis and more precise diagnostics of cardiac or brain disorders based on detailed high resolution registration of surface biopotentials distribution. In electrocardiography, forty years of experience with BSP showed that information contents in maps obtained from 24 to 240 leads is greater than that of commonly used standard 12-lead ECG of Frank VCG. Measured maps can be used for immediate analysis and diagnostics, however, model studies prove that BSP maps together with information on torso or head structure obtained from imaging techniques such as MRI, CT or ultrasound systems can be used for more advanced diagnostic methods [6] enabling noninvasive assessment of normal or abnormal electrical sources in the underlying tissues. Besides simple surface potential maps, 2D or 3D distributions of another parameters derived from measured potentials are often used to quantify the properties of the biopotential fields. As an example, body surface integral maps

displaying the surface distribution of integrals of surface ECG potentials over the ventricular depolarizationrepolarization period (QRST interval in ECG) practically depend only on the action potentials and not on the ventricular activation sequence [1]. Moreover, measured changes in surface QRST integral maps together with the knowledge of torso geometry and electrical properties can thus serve as input data for noninvasive assessment of ischemic heart regions. In this paper, a powerful high resolution biopotential measuring system that can be used for advanced BSP-based cardiac or brain studies is presented and its ability to noninvasively locate an ischemic heart region with changed repolarization using differences in surface integral maps and model of the patient torso is demonstrated. II. METHODS AND MATERIALS Multichannel biopotentials were measured from the chest surface using a new high resolution BSP mapping device. Differences in integral maps of ECG potentials and dipole model of the cardiac electric generator in realistic inhomogeneous human torso were used to identify local ischemic changes in the myocardium. A. Biopotential mapping device Based on previous experience with BSP mapping devices [2,3] a battery powered biopotential mapping system ProBio-8 (Fig.1) was developed to obtain high quality multichannel ECG recordings. The system consists of a data acquisition unit and a standard personal computer used for measurement control, data processing and analysis, diagnostic data interpretation and human interface. The data acquisition system is modular and can be configured for up to 128 channels. One input module with two 24-pin Centronix connectors contains 16 input channels. The multi-channel amplifying and measuring unit is placed in a patient terminal box and connected over Ethernet (wired or wireless) to the host personal computer.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 86–89, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

Flexible Multichannel System for Bioelectrical Fields Analysis

Fig. 1. Block scheme of the biopotential mapping device

Fig. 2.

Active electrodes formed by a disposable Ag-AgCl electrode and an active adapter a snap connection and a flexible multi-wire cable are used as biopotential sensors.

Small geometric dimensions and metallic shielding of the patient terminal minimize the capacitive coupling with the environment in the examination room and allow high quality of measured signals. Biosignals are sensed by disposable Ag-AgCl electrodes with active adapters connected to the electrode snaps (Fig.2). Low output impedance of the active electrodes reduces the possible disturbing signals induced in the electrode cables. Signals are measured relative to a common mode sense (CMS) electrode that can be attached to the patient so that the interference from the common mode is minimal. Active neutralization of the patient using a driven electrode (DRL) is also employed. Besides further reduction of the common mode voltage, the current limiting resistor in the DRL protects the patient against defects in the amplifiers. In the worst case that one of the active electrodes would

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break down and become shorted to the power supply voltage, function of the DRL results in a maximum error current of 50 μA which complies with the value specified for the IEC-60l CF type isolation used in Europe (the value of the resistor can be changed to achieve maximum of 10 μA error current and to comply with the US standards). Additional patient protection has been implemented in the data acquisition module. It prevents dangerous currents to flow through the patient body despite of the DRL current limiter in the very unlikely case that two electrodes would fail one after the other and electrodes would be connected to opposite power supply rails. This protection only enables the power supply if no errors are detected. Each measuring channel is equipped by a DC-coupled ECG amplifier with a fixed gain of 100 and a 22-bit Δ-δ A/D converter. Sampling frequency up to 2000 Hz can be selected. The data acquisition system is equipped with a 16/32-bit 600MHz high performance Analog Devices ADSP-BF537 processor with embedded network connectivity. Processor streams the sampled serial data from selected channels to the host PC and its DSP properties enable additional real-time biosignal processing. Selection of measured channels, real-time operations and proper formatting of acquired data with several possible byte lengths is controlled by commands received from the host PC computer. Communication with the host PC over Ethernet provides easy data transfer to or from the host with data rates of up to 10/100 Mbps (wired) or 54 Mbps (wireless). The patient terminal is powered by a rechargeable Li-Ion battery module. Due to the advanced power management controlled by the by the on-board controller the system can work up to one whole working day before the battery has to be recharged. Application software developed on the Microsoft .NET platform is running under Windows/XP. Real-time ECG data acquisition program enables to set the working mode of the data acquisition subsystem, to check the electrode contacts and to read, and store the stream of measured data. Biosignals from selected channels can be simultaneously on-line processed and monitored on the PC screen. Data acquisition is followed by modular off-line application-specific data processing written in several environments (Matlab, C#). It includes signal processing with time and frequency analysis, 2D or 3D biopotential mapping and consequent biophysical evaluation and medical interpretation of the measured data. B. Noninvasive assessment of cardiac ischemia As an example of using the measured ECG data, a noninvasive method for assessment of local ischemic regions is

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presented. It interprets the measured surface ECG maps and evaluates changes in QRST integrals caused by local ischemia. These changes can be interpreted as being caused by sources originating in the ischemic heart region due to changed action potentials during the repolarization phase. For a small heart region, such sources can be represented by a single equivalent current dipole (ECD) located at the centre of the region and a fixed dipole model located at one of predefined positions within the ventricular myocardium can be used. The equivalent dipole can be inversely estimated as Mi = Ti+ Φ

for i=1, 2, ...n

were calculated. It was hypothesized that this difference appeared because some ischemic regions around the infracted tissue disappeared after the treatment due to the revascularization. To assess approximate position of the changed region, inhomogeneous torso model with lungs and heart (Fig. 3) was used in all patients and ECD representing the region with changed repolarization was located by dividing the ventricular volume into 28 segments and supposing possible positions of the ECD at their gravity centers (see Fig. 5).

(1)

where Mi is estimated integral of the dipole moment of a dipole located at the i-th predefined position in the myocardium, Φ are differences of QRST integrals in m measured surface points, and Ti+ is a pseudo-inverse of the transfer matrix between the i-th dipole and potentials in m surface points that depends only on the geometry and electrical properties of the torso. Minimal rms difference between measured QRST integrals and integrals produced by dipoles estimated at each of the i positions can be used as a criterion for finding the best ECD representing the ischemic changes in the QRST integrals.

III. RESULTS Measured and calculated surface distributions can be viewed as numerical tables, 2D or 3D maps, animation of maps as well as displaying of 3D maps at desired viewing angle is possible (Fig. 4). In patients measured before and after PCI there were noticeable changes in QRST integral maps. In 8 of them the measured difference integral maps could be reasonably represented by maps generated by single current dipole. In 6 cases the relative rms error between measured and approximated difference integral map was < 35%, in another 2 patients it was <

C. Experimental data Using the presented measuring device, possibility to assess the heart region with changed repolarization was tested on a group of patients after myocardial infarction (8 men and 3 women, age 45−69) that later underwent successful percutaneous cardiac intervention (PCI) on one of the main coronary arteries (8 on left anterior descending artery, 1 on right circumflex artery and 2 on right coronary artery). ECG signals were measured from 32 leads according to Lux before and after the PCI. Surface QRST integral maps were computed from both measurements and difference maps

Fig. 3.

Inhomogeneous torso model with lungs and heart was used to locate the ischemic lesions.

Fig. 4. Two examples of body surface distributions displayed by the measuring device: Top: 2D QRST integral map of a patient with an old inferior myocardial infarction, step in map is in [mV.ms]. Bottom: one 3D view of a body surface potential map in a patient with an old anterior MI, step in map is in [mV].

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Flexible Multichannel System for Bioelectrical Fields Analysis

56%. In remaining 3 patients rms error of the approximation was higher and they were excluded from further analysis. Despite the use of a common torso model, in 7 of 8 analyzed patients positions of estimated ECD matched the region supplied by the treated vessel or at least they were located nearby the anterior or postero-lateral wall of the left ventricle. Dipole moments were directed inwards the ventricle and suggested changes near the endocardial surface. In 1 patient after PCI on RCA the ECD was incorrectly located in mid anterior left ventricular wall. In Fig. 5 there is an example of measured integral maps before and after the PCI on the left anterior descending artery (LAD) and corresponding difference integral map. Estimated ECD location on the anterior left ventricular wall as identified using an analytical heart model is shown in the right panel.

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model, small ischemic lesions could be located with a mean error of 9±4 mm, in larger or transmural lesions the error was 17±14 mm what suggests that more sophisticated model should be used. ECD localization from 62 or 192 leads provided slightly better results than from 32 ECG leads and more than 32 leads should be used in the future. Our experiments indicate that the ProBio 8 device with its application software could be a useful tool for noninvasive cardiac diagnostics. Using model-based interpretation of differences in QRST integral maps it enables to identify small ischemic heart regions with changed repolarization. Using this method, acceptable localization of equivalent current dipole representing the revascularized region was obtained in 7 of 11 MI patients after PCI.

ACKNOWLEDGMENT

IV. DISCUSSION AND CONCLUSIONS Experience with ischemic changes in surface potential distribution shows that they are relatively small when compared with normal inter-individual fluctuations and can hardly be detected as departures from mean normal maps [3]. Therefore the possibility to identify ischemic heart regions using modelbased interpretation of differences in individual QRST integral maps was tested on simulated data [4]. The simulations showed that changes in QRST integral maps were greater than observed intra-individual variability, what, in principle, allows their identification. Using inhomogeneous torso

This work was supported by grants 2/4089/24 from the VEGA grant agency, APVV-20-059005 from the Slovak APVV agency and MSM 6840770012 from the Ministry of Education, Youth and Sports of the Czech Republic.

REFERENCES 1. 2. 3.

4.

5. 6.

Trudel MC et al. (2004) Simulation of QRST Integral Maps with a Membrane-Based Computer Heart Model Employing Parallel Processing. IEEE Trans on BME, 51: 1319-1329. Rosik V, Tysler M, Jurko S, Raso R, Turzova M (2002) Cardio 7 Portable System for High Resolution ECG Mapping. Studies in Health Technology and Informatics. 90: 41-46. Tysler M, Rosik V, Kneppo P (2006), Multichannel ECG Measurement for Noninvasive Identification of Heart Regions with Changed Repolarization. Proceedings of the XVIII IMEKO World Congress, Rio de Janeiro, Brazil (in press). Filipova S, Tysler M, Turzova M, Rosik V (2003) Reference ECGmapping etalons improve the diagnostic accuracy of myocardial ischemia according to departure isointegral surface maps. Int. J. of Bioelectromagnetism, 5(1): 369-370. Tysler M, Turzova M, Svehlikova J, Heblakova E, Filipova S (2005) Noninvasive detection of ischemic regions in the heart. IFMBE European Conference on BME. IFMBE Proceedings, 11: 2207. Zivcak J, Hudak R (2001) Biomechanizmy. Grafotlac, Presov. Address of the corresponding author:

Fig. 5.

Left: QRST integral maps in a 69 years old female patient after MI (95% stenosis of RIA) before and after the PCI on LAD (top, center left) and corresponding difference map (bottom left). Step in maps is 12 mV.ms, zero isointergral line is marked in black. Right: Realistic heart model with marked position of the calculated equivalent current dipole representing the region with repolarization changes caused by PCI.

Author: Prof. Peter Kneppo Institute: Faculty of Biomedical Engineering, Czech Technical University in Prague, Street: Nam. Sitna 3105 City: 272 01 Kladno Country: Czech Republic Email: [email protected]

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FPGA-based System for ECG Beat Detection and Classification M. Cvikl1 and A. Zemva2 1

2

Iskra Sistemi, d.d. / Power System Protection and Control, Ljubljana, Slovenia Faculty of Electrical Engineering / Departement of Electronics, Laboratory for Integrated Circuit Design, Ljubljana, Slovenia

Abstract— We present a Field Programmable Gate Arraybased system for single-lead electrocardiogram signal processing which performs beat detection and classification to normal and ventricular beats. Geometrical properties of a phase-space portrait of an ECG signal are used for QRS complex detection, while classification is done with a modified classification algorithm that is a part of the Open Source ECG Analysis Software. The chosen Field Programmable Gate Array has an embedded PowerPC processor and is very suitable for mixed hardware and software designs. Beat detection is implemented in hardware and the classification is executed on the embedded PowerPC 405 core. The algorithm was developed on the MITBIH Arrhythmia Database resampled to 250 samples per second. Sensitivity of 99.80% and positive predictivity of 99.84% was achieved for QRS complex detection and sensitivity of 92.59% and positive predictivity of 95.55% was achieved for identification of premature ventricular complexes. A comparison of processing speed between a personal computer and the embedded system shows that a personal computer running at 18-times faster clock speed processes data only six times faster.

A. QRS Complex Detection Algorithm The algorithm is suitable for hardware (HW) implementation and is designed to processes blocks of ECG signal [2]. The QRS complex detection is based on geometrical properties of two-dimensional (2D) phase-space portrait of the ECG signal. QRS complexes are found based on the size of the polygon bounded by a number of consecutive data points. To minimize the effects of noise and artifacts on QRS complex detection, a prefiltered ECG signal is used for phase-space portrait generation. A nonrecursive band-pass filter with integer multipliers [3] was chosen, that emphasizes QRS complexes, attenuates high-frequency and lowfrequency components and has zero gain at 50 Hz and its multiples at 250 Hz sampling rate. An input ECG signal and the filtered signal are shown in Figure 1(a) and Figure 1(b).

Keywords— ECG, detection, classification, FPGA

I. INTRODUCTION Electrocardiogram (ECG) is an important clinical tool for the diagnosis of heart diseases. Reliable real-time detection and classification of heartbeats is an important task in detection of cardiac arrhythmias, which can save one's life, but requires a lot of processing speed. In this paper we describe a system with high processing speed which detects heartbeats and classifies them to either normal or ventricular. The entire system is built on a Field Programmable Gate Array (FPGA) with an external Double Data Rate (DDR) memory used for testing purposes. QRS complex detection and beat classification performance on the MIT-BIH Arrhythmia Database [1] is presented and its processing speed compared with a personal computer (PC). II. THE ALGORITHM The algorithm is partitioned into QRS complex detection algorithm and classification algorithm and is designed for block processing of an ECG signal sampled at 250 Hz.

Fig. 1 ECG signal processing stages: (a) input ECG signal, (b) band-pass filtered ECG signal, (c) phase-space portrait, (d) calculated areas

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 66–69, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

FPGA-based System for ECG Beat Detection and Classification

The 2D phase-space portrait is created using the method of delays, where the same data is plotted in each dimension, but with certain time delay (τ) between them. A phase-space portrait of a band-pass filtered signal using a time delay of 20 ms is shown in Figure 1(c). Two larger polygons created by the two QRS complexes and two smaller polygons that were created by P waves can be observed. The size of an area formed by eight data points is used as a detection function for locating QRS complexes. For this purpose, the coordinates of each data point in the 2D space are written as:

x [ nT ] =ECG[nT], y [ nT ] =ECG[(n − τ )T] (1) The area is calculated using plane geometry equation for a planar non-self-intersecting polygon:

1⎛ x Area = ⎜ 1 2 ⎝ y1

x2 x2 + y2 y2

x3 x + ...+ 7 y3 y7

x8 ⎞ ⎟ (2) y8 ⎠

The detection function obtained using the above equation for the ECG signal from Figure 1(a) is shown in Figure 1(d), where the two QRS complexes are easily spotted and the time of their occurrence is easily determined. QRS complexes are detected by comparing the detection function to a detection threshold level that is calculated in the next step. The level is set to four times the average value of the detection function in the current block. All peaks that exceed this level are treated as candidates for being created by QRS complexes. Once the peaks are located, the following two sets of criteria are used to improve QRS complex detection: 1. Search back rule: If the distance between two consecutive peaks is greater than 150% of previous R-to-R interval, an additional search is performed to discover if any peaks with lower amplitude were missed in the initial search. This search is therefore performed with the QRS complex detection threshold level lowered to 50% of its initial value. 2. Peak distance and Peak height: If consecutive peaks are less than 200 ms apart, then the highest of the peaks is considered to originate from a QRS complex. If the distance between peaks is more than 200 ms, both peaks are considered to originate from a QRS complex. To correctly process blocks of ECG signal and to minimize the risk of missing QRS complexes that might be located at the end of the block, an additional mechanism is used for block overlapping. A block-length of 700 samples is used for processing and the mechanism sets the last detected QRS complex in the current block as the starting point of the next block. If no QRS complexes are detected,

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consecutive blocks are overlapped by 250 samples, so the last part of the block is again processed in the next block. B. Classification Algorithm The classification algorithm is taken up from the set of the Open Source ECG Analysis SW (OSEA) [4]. Characteristics like QRS complex width, R-to-R interval, noise presence and matching to previously classified beat types are combined with a set of rules and used to classify the beats as either normal or ventricular (PVC). Due to different approach to beat detection from the one used in the OSEA, the classification algorithm had to be modified. A one-second frame of ECG signal originally used for classification can not be provided for every detected beat, thus the frame is reduced to half of that length. For similar reasons, low frequency noise measurements originally used in the algorithm are excluded. Additionally, the embedded PowerPC processor (PPC) does not support floating point operations in HW, therefore only integer arithmetic is used in the algorithm; all floating point variables and constants are multiplied by 256 and all functions that perform calculations with such variables are altered in such a way, that calculation error caused by type-translation is minimized, i.e. assuring that multiplications are performed before division, especially if the result of multiplication is used as denominator. These modifications somewhat degraded classification performance, yet this was mitigated by changing some of the other parts of the classification algorithm; the intervals checked by the RRmatch and RRshort2 functions for rhythm classification were slightly extended, while the estimation of high frequency noise was modified to use a shorter interval. III. SYSTEM DESIGN A development system with Virtex-II PRO XC2VP30FF896-7 [5] Field Programmable Gate Array (FPGA) was used for system implementation. The detection algorithm is designed in such a way, that band-pass filtering, detection function calculation and QRS complex detection threshold level calculation can be formed into a pipeline. FPGAs make it possible that the elements of such a pipeline are independently and simultaneously executed. Moreover, 14 multiplications per data point required for area calculation can also be executed in parallel, which additionally increases the processing speed. For these reasons the QRS complex detection algorithm was implemented in HW, while the classification was assigned to one of the embedded PPC.

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M. Cvikl and A. Zemva

The system architecture is shown in Figure 2. PPC program and data are stored in a Block RAM (BRAM) connected to a high-performance 64-bit Processor Local Bus (PLB). The DDR memory is also connected to the PLB bus and is used for ECG signal storage and also as storage for beat detection and classification results. Peripheral devices like Universal Asynchronous Receiver-Transmitter (UART) and SysACE controller are connected to a low-performance 32-bit On-chip Peripheral Bus (OPB) and the PPC can access them through a PLB to OPB bridge. The UART interface provides communication with a personal computer (PC). Information about processing speed, QRS complex detection and classification, as well as information used for debugging are sent through it. The SysACE controller enables read/write operations from or to a CompactFlash (CF) device. Currently, the system is designed so that a CF memory card serves as a source of ECG data. For easier evaluation and comparison of processing speed, every ECG record used for beat detection and classification performance testing is first copied from the CF to the DDR memory. In the QRS complex detection phase the PPC copies a block of data from the DDR memory to a dual-port BRAM connected to the Data-Side On-Chip Memory (DSOCM) bus. The PPC then starts the beat detection process through the user logic control module connected on the OPB. The detection logic reads the ECG data from the second data port, executes beat detection and writes the results back to the BRAM for the PPC to access them. Additional information about task completion and the number of detected beats, if any, is passed to the user logic control module.

After the task completion is detected, the detected beats are classified and the results are written back to the DDR memory. IV. RESULTS AND DISCUSSION Two types of performance tests were made for our algorithm. QRS complex detection and classification performance evaluation of our algorithm was performed on a PC and on the embedded system and results compared with those of the OSEA beat detection and classification algorithm. Several versions of our algorithm were tested to see how performance is influenced by parameter reductions or optimizations. ECG data from all 48 records of the MIT-BIH Arrhythmia Database was resampled to 250 samples per second and used for performance evaluation of all algorithms and their implementations. QRS complex detection sensitivity (Q Se), positive predictivity (Q +P), PVC sensitivity (V Se) and PVC positive predictivity (V +P) of the algorithms were evaluated using ecgeval tool available in WFDB library [6]. Table 1 shows the QRS complex detection and classification performance results as reported by the ecgeval tool. The algorithms DetClas PC1 and DetClas PC2 are combinations of our QRS complex detection algorithm and different versions of the beat classification algorithm. Both algorithms were tested on a PC. The DetClas PC1 algorithm includes an unmodified classification algorithm, which has access to all data that is originally needed for beat classification. Better QRS complex detection and somewhat better classification of our algorithm than of the OSEA algorithm can be observed. The DetClas PC2 algorithm includes a modified version of beat classification algorithm, where the classification algorithm can only access ECG data in the processed block. A slight improvement in PVC sensitivity, but a decrease in predictivity can be observed. The DetClas Embedded presents the results of the algorithm that was implemented in the FPGA. QRS complex Table 1

Fig. 2 System architecture

QRS complex detection and classification performance on MIT-BIH Arrhythmia Database

Algorithm

Q Se [%]

Q +P [%]

V Se [%]

V +P [%]

OSEA

99.76

99.80

91.87

95.93

DetClas PC1

99.84

99.81

91.85

96.27

DetClas PC2

99.84

99.81

92.05

95.19

DetClas Embedded

99.80

99.84

92.59

95.55

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FPGA-based System for ECG Beat Detection and Classification

Table 2

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V. CONCLUSION

Processing speed for two MIT-BIH records

Record No.

PC [s]

Embedded [s]

Time Factor

117

0.344

1.057

3.07

208

0.594

5.850

9.85

detection performance is different from the algorithm implemented in SW, which also causes changes in classification performance. The HW implementation of the QRS complex detection algorithm is not an exact copy of the SW algorithm; the area calculation part of the QRS complex detection algorithm is partitioned to several parts to achieve higher processing speed. As a result, a different signal delay is introduced which affects the content of a data block and thus a difference in calculated QRS complex detection levels. A processing speed comparison test was also performed, where the time needed to process one ECG record was measured. Processing speed of the embedded system with the operating frequency of all components set at 100 MHz was compared with the processing speed of an AMD Athlon 2500+ processor with the operating frequency set at 1.83 GHz and the operating frequency of DDR memory set at 166 MHz. Both systems fetched the ECG data from the DDR memory and returned the beat detection and classification results to the DDR memory. A time factor was calculated as a ratio between the processing times of the embedded system and of the PC. The test results for two records are shown in Table 2. Among 48 records, these records exhibit the largest difference between the time factors. The reasons for this are functions used for calculation of beat matching in the classification algorithm; in contrast to record 117, these functions are heavily utilized in record 208 which is in favor of the AMD Athlon processor. Even though the AMD Athlon runs at 18-times higher clock frequency and its DDR memory interface at 1.6-times higher frequency, the embedded system processed the same data 9.85-times slower in the worst case and 5.95-times slower in average. Considering that the operating frequency of the PPC can be raised by three times and the operating frequency of the DSOCM interface by 50%, we expect to reduce this difference. Additionally, several tasks performed by the PPC could be implemented in HW and performed in parallel, including the BeatCompare function that consumes most of the processing time.

This paper presents an FPGA-based system for beat detection and classification in electrocardiograms with the QRS complex detection algorithm implemented in hardware and beat classification performed on an embedded PowerPC processor. Beat detection and classification performance was evaluated on the MIT-BIH Arrhythmia Database where good results were achieved. Although the processing speed performance is lower than of a personal computer, it is still high and it can be further improved in several ways. Due to FPGA reconfigurability, our system can be upgraded with additional communication and processing blocks and therefore used in various low-cost systems where high-speed processing of ECG signal is required.

ACKNOWLEDGEMENT This work is supported by the Ministry of Higher Education, Science and Technology of the Republic of Slovenia under grant 3211-05-000588.

REFERENCES 1.

2.

3. 4. 5. 6.

Mark R G, Schluter P S, Moody G B et al. (1982) An annotated ECG database for evaluating arrhythmia detectors, Proc. 4th Annu. Conf. IEEE EMBS on Frontiers of Engineering in Health Care 1982, IEEE Computer Society Press, pp 205–210 Cvikl M, Jager F, Žemva A (2007) Hardware Implementation of a Modified Delay-Coordinate Mapping-Based QRS Complex Detection Algorithm. EURASIP Journal on Advances in Signal Processing, in press Jager F (1982) QRS Complex Detection in Electrocardiogram. M. Sc. Thesis, University of Ljubljana, Ljubljana Hamilton P (2002) Open Source ECG Analysis. Computers in Cardiology, vol. 29, pp 101-104 Xilinx XUP Virtex II Pro Development System at http://www.xilinx.com/univ/xupv2p.html Moody G B (2005) WFDB Programmer's Guide (Tenth Edition) at http://www.physionet.org/physiotools/wpg/wpg.htm Author: Institute: Street: City: Country: Email:

Andrej Zemva Faculty of Electrical Engineering Trzaska ulica 25 Ljubljana Slovenia [email protected]

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Fractal analysis of heart rate variability in COPD patients G. D’Addio1, A. Accardo2, G. Corbi3, N. Ferrara1, F. Rengo1 1

S. Maugeri Foundation, IRCCS, Rehabilitation Institute of Telese-Campoli, Italy 2 DEEI, University of Trieste, Italy 3 Dpt of Health Sciences, University of Molise, Italy

Abstract— Non linear parameters obtained from HRV analysis has recently been recognized to provide valuable information for physiological interpretation of heart rate fluctuation. Among the numerous non-linear parameters related to the fractal behaviour of the HRV signal, two classes have gained wide interest in the last years: the beta exponent based on the 1/f-like relationship, starting from the spectral power, and that based on fractal dimension (FD). In order to evaluate whether the FD is capable of discriminating HRV impairment between COPD patients and normal, 16 pathological and 10 healthy subjects were studied. All subjects underwent 24-hour Holter recording analysed by fractal and 1/f-like techniques. Differently from methods usually used in literature to evaluate the fractal dimension, in this work the FD was extracted by using the Higuchi's algorithm that permits to calculate the parameter directly from the HRV sequences in the time domain. Results show that fractal analysis contains relevant information related to different HRV dynamics that permits to separate normal subjects from COPD patients. Keywords— HRV, COPD patients, fractal analysis

I. INTRODUCTION Respiratory arrhythmia, which is the cyclical decrease and increase in heart period synchronous with breathing and disappearing after pharmacological blockade of autonomic ganglion transmission [1], represents the most recognizable evidence of a functional link between neural cardiac and respiratory controls [2]. The analysis of heart rate variability (HRV) is a wellrecognized tool in the investigation of the autonomic control of the heart [3]. Limited data, however, are available on the use of HRV in the assessment of the autonomic imbalance in patients with chronic obstructive pulmonary disease (COPD). The respiratory modulation of RR interval can be explored with various methodologies, and it has been shown that respiratory arrhythmia is modulated by both the amplitude and frequency of respiration [4,5] and is decreased in clinical conditions, such as myocardial infarction [6] and congestive heart failure [7]. The little that is known about cardiac autonomic function in patients with COPD suggests that it is adversely affected, as reflected in reduced HRV [8].

Thus, Holter recordings in a younger COPD patient population without coronary artery disease would help clarify the effect of COPD by itself on cardiac rate, rhythm, and autonomic tone. Also, it is possible that measurement of HRV will have prognostic value in patients with COPD. Decreased HRV associated with altered cardiac autonomic modulation is associated with an increased risk of cardiac events in clinically disease-free subjects, even after adjusting for known risk factors [9]. Decreased HRV is also associated with increased mortality in patients after myocardial infarction and in many other patient populations [6]. Changes in respiratory patterns and lung volumes may influence the autonomic outflows by complex reflex adjustments, mediated by both vagal and sympathetic efferent activity, as well [10]. Little is known about the effects of long term changes in respiratory patterns and volumes, as can occur on HRV in COPD. Among non-linear methods proposed so far to measure the fractal behaviour of the HRV signal, that based on the beta exponent of the 1/f-like relationship, starting from the spectral power [11-14], and that based on the fractal dimension (FD), have gained wide interest in the last years. The latter has traditionally been approached following the chaos-theory, with the aim of modelling the attractor extracted from HRV sequences [15], and the FD parameter has usually been estimated from the slope of the 1/f relationship [16]. However, the FD can also be directly extracted from HRV sequences by means of many methods [17,18]. In this work we followed this approach, using the FD estimated by the Higuchi algorithm [17]. This method allows a better fractal estimation, eliminating the errors due to the indirect estimation of FD from the spectral power. The aim of this study was to assess whether the Higuchi’s FD is capable of discriminating COPD patients from normal subjects. Results were compared with those obtained from the classical beta exponent.

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Fractal analysis of heart rate variability in COPD patients

C. Fractal dimension analysis

II. MATERIALS AND METHODS A. Subjects We studied 16 male patients consecutively admitted to Pneumology Rehabilitation Division of “Salvatore Maugeri” Foundation, institute of "Telese Terme" (Table 1). All enrolled subjects had a positive medical history for COPD. Patients were considered to be affected by COPD if they fulfilled either of the following criteria: 1) they had an FEV1/FVC of <70% and no change or an FEV1 increase of >12%, but not FEV1 normalisation after 100 mg fenoterol; or 2) they nor reported history of wheeze in the last year, had an FEV1/FVC of <70%, an FEV1 of <80% and an FEV1 increase of <12% after 100 mg fenoterol. The control group (N) consisted of 10 healthy subjects (age 45±5). Table 1: Descriptive statistics of some variables in COPD population (age; gender; SO2, oxygen saturation; FEV1, forced expiratory volume in one second; HR, heart rate; EF, ejection fraction; SBP, systolic blood pressure; DBP, diastolic blood pressure)

variables

79

variables 13 (82%)

age

72±9

gender (M) (%)

SO2

88±6

FEV1

2.4±0.5

HR

83±8

EF

60±7

SBP

127±15

DBP

74±6

B. Holter analysis The study population underwent a 24-hour Holter ECG recording by a portable three-channel tape recorder, processed by a Marquette 8000 T system with a sampling frequency of 128 Hz. In order to be considered eligible for the study, each recording had to have at least 12 hours of analyzable RR intervals in sinus rhythm. Moreover, this period had to include at least half of the nighttime (from 00:00 AM trough to 5:00 AM) and half of the daytime (from 7:30 AM trough to 11:30 AM) [19]. Before analysis, identified RR time series were preprocessed according to the following criteria: 1) RR intervals associated with single or multiple ectopic beats or artifactswere automatically replaced by means of an interpolating algorithm, 2) RR values differing from the preceding one more than a prefixed threshold were replaced in the same way as for artefacts. The RR time series were finally interpolated by piecewise cubic spline and resampled at 2 Hz.

Fractal dimension was calculated by using the Higuchi's algorithm [17]. From a given time series X(1), X(2), ... X(N), the algorithm constructs k new time series; each of them, Xmk, is defined as Xmk:X(m),X(m+k),X(m+2*k),..., X(m+int((N-m)/k)*k) where m=1,2,...,k and k are integers indicating the initial time and the interval time, respectively. Then the length, Lm(k), of each curve Xmk is calculated and the length of the original curve for the time interval k, L(k), is estimated as the mean of the k values Lm(k) for m=1, 2, ..., k. If the L(k) value is proportional to k-D, the curve is fractal-like with the dimension D. Then, if L(k) is plotted against k, for k ranging from 1 to kmax, on a double logarithmic scale, the data should fall on a straight line with a slope equal to -D. Thus, by means of a least-square linear best-fitting procedure applied to the series of pairs (k, L(k)), obtained by increasing the k value, the angular coefficient of the linear regression of the graph ln(L(k)) vs. ln(1/k), which constitutes the D estimation, is calculated (Fig. 1). D. Beta exponent analysis Power law beta exponent was calculated from the power spectral density function estimated by the Blackman-Tukey method after linear trend removal. The beta index represents the slope (Figure 2) of the linear fit in the very low frequency band (<0.05 Hz) of the log(power) on log(frequency) relationship [20].

Fig. 1. Example of an hi sequence determination on a curve for the length calculation. The values of hi were calculated as |X(m+i*k) -

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G. D’Addio, A. Accardo, G. Corbi, N. Ferrara, F. Rengo

Fig. 2. Example of the beta exponent evaluation by means of the slope of the linear best fitting (dashed line) of the power spectrum for frequencies < 0.05Hz.

III. RESULTS Descriptive statistics for the FD and beta exponent parameters in the two study group are reported in table 2. The normality of the distribution of HRV variables was assessed by the Shapiro-Wilks test. Between-group comparisons were carried out by the analysis of variance (t-Student test for unpaired data). The Higuchi's FD parameter showed a marked, highly significant, increase in the mean value passing from normal to patients with COPD. Conversely, the beta exponent did not reach statistical significance between the two studied groups. Table 2: Mean±SD of Higuchi's fractal dimension (FD) and beta exponent

The sensitivity of the FD and beta exponent parameters to the severity of the central nervous system damage, however, appears to be different. Indeed, the Higuchi's index strongly changes passing from normal to pathological subjects. The beta exponent, on the contrary, seems rather insensitive to changes in autonomic cardiovascular regulation brought about by COPD. These findings suggest that, although the two algorithms try to measure the same fractal property of HRV, they provide non superimposable results. This could be due to the fact that the beta exponent is usually calculated considering only the low band of the signal (<0.05 Hz). Probably the changes in autonomic cardiovascular regulation much more affect a band with higher frequency. Scarce are the studies in COPD patients that, using linear and spectral indexes of HRV, found association between COPD and impaired autonomic regulation [21,22], while it has not yet completely investigated if nonlinear analysis of HRV might provide more valuable information for the physiological interpretation of heart rate fluctuations and for the risk assessment in these patients. A major limitation of this study is the low sample size of the studied groups. Therefore our findings should be interpreted as purely exploratory. Nevertheless, they clearly suggest that fractal analysis contains relevant information related to different heart rate variability dynamics of COPD subjects, candidating this approach for future risk assessment studies of these patients.

REFERENCES 1 2

3

calculated on two study groups.N: normal subjects; COPD : chronic ostructive pulmonary disease patients.

FD Beta

N 1.354 ± 0.057 -1.016 ± 0.138

COPD 1.684 ± 0.111 -1.061 ± 0.197

p <0.0001 0.53

IV. DISCUSSION These preliminary results indicate that fractal indexes reflect the impairment of the autonomic nervous system in patients with COPD.

4 5 6

Rimoldi O, Pierini S, Ferrari A, Cerutti S, Pagani M, Malliani A. Analysis of short-term oscillations of R-R and arterial pressure in conscious dogs. Am J Physiol 1990;258:H967-H976 Koepchen HP, Abel HH, Kliibendorf D. Central cardiorespiratory organisation. In: K. Myakawa et al (Eda.). History of Studies and Concepts. Mechanisms of blood pressure waves, Japan Sci. Sot. Press, Springer Verlag, Tokyo. Berlin, 1984, pp. 3-23. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart Rate Variability – Standard of Measurement, Physiological Interpretation and Clinical Use. Circulation 1996;93:104365 Angelone A, Coulter NA. Jr. Respiratory arrhythmia: a frequency dependent phenomenon, J. Appl. Physio l1964;19:479482 Hirsch JA, Bishop B. Respiratory smus arrhythmia in humans: how breathing pattern modulates heart rate. Am J Physiol 1981;241:H620-H629 Kleiger RE, Miller JP, Bigger JT, Moss AR. Multicenter PostInfarction Research Group. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 1987;59:256-262

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11

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Saul JP, Arai Y, Berger RD, Lilly LS, Colucci WS, Cohen RJ., Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis, Am J Cardiol 1988;61:1292-1299 Volterrani M, Scalvini S, Mazzuero G, et al. Decreased heart rate variability in patients with chronic obstructive pulmonary disease. Chest 1994;106:1432-37 Tsuji H, Lawrence MG, Venditti FJ Jr, et al. Impact of reduced heart rate variability on risk for cardiac events: the Framingham Heart Study. Circulation 1996; 94:2850-55 De Burgh Daly M. Interactions between respiration and circulation. In: A.P. Fishman, N.S. Cherniack, J.G. Widdicombe and S.R. Geiger (Eds.), Handbook of Physiology. The Respiratory System, Section 3. American Physiological Society. Bethesda. MD, 1986, pp.529-594 Saul JP, Albrecht P, Berger RD, Cohen RJ. Analysis of long term HRV: methods, 1/f scaling and implications. In: Computers in Cardiology 1987. IEEE Computer Society Press, 1987:419-22. Bigger T, Steinman R, Rolnitzky L, Fleiss J, Albrecht P, Cohen R. Power law behavior of RR-Interval Variability in healthy middle-aged persons, patients with recent acute myocardial infarction and patient with hearth transplants. Circulation 1996;93:2142-51. Makikallio TH, Hoiber S, Kober L, Torp-Pedersen C, Peng CK, Goldberger AL, Huikuri HV. Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation. Am J Cardiol 1999;83(6):836-9. Makikallio TH, Huikuri HV, Hintze U, Videbaek J, Mitrani RD, Castellanos A,Myerburg RJ, Moller M. Fractal analysis and time- and frequency-domain measures of heart rate variability as predictors of mortality in patients with heart failure. Am J Cardiol 2001;87(2):178-82. Cerutti S, Carrault G, Cluitmans PJ, Kinie A, Lipping T, Nikolaidis N, Pitas I, Signorini MG. Non-linear algorithms for proc-

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essing biological signals. Comp Met Prog Biomed 1996;1:5173. Butler GC, Yamamoto Y, Xing HC, Northey DR, Hughson RL. Heart rate variability and fractal dimension during orthostatic challenges. J Appl Physiol 1993;75(6):2602-12. Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D 1988;31:277-83. Goldberger AL. Fractal mechanisms in the electrophysiology of the heart. IEEE Eng Med Biol 1992;11:47-52. Bigger T, Fleiss J, Rolnitzky L, Steinman R. Stability over time of heart period variability in patients with previous myocardial infarction and ventricular arrhythmias. Am J Cardiology 1992;69:718-23. Hikuri HV, Makikallio TH, Airaksinen KEJ, Seppanen T, Puikaka P, Raiha IJ, Sourander LB. Power-law relationship of heart rate variability as a predictor of mortality in the elderly. Circulation 1998;97:2031-36. Pagani M, Lucini D, Pizzinelli P, Sergi M, Mela GS, Malliani A. Effects of aging and of chronic obstructive pulmonary disease on RR interval variability J Auton Nervous System l996;59:125-132 Volterrani M, Scalvini S, Mazzero G, Lanfranchi P, Colombo R, Clark AL, Levi G. Decreased Heart Rate Variability in Patients With Chronic Obstructive Pulmonary Disease Chest 1994;106:1432-37

address of the corresponding author: Author: Institute: Street: City: Country: Email:

Gianni D’Addio S. Maugeri Foundation – Bioengeneering Dpt Via Bagni Vecchi 82037 Telese Terme (BN) Italy [email protected]

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Intelligent Internet Based, High Quality ECG Analysis for Clinical Trials T.K. Zywietz1, R. Fischer2 1

Biosigna – Medical Diagnostics, Munich, Germany 2 Dept. of Biometrics, MHH, Hannover, Germany

Abstract— Drugs in general have the ability to cause delay of cardiac repolarization which can be measured as prolongation of the QT-interval in the ECG. A delay in cardiac repolarization can lead to so called Torsade de Pointes (TdP) and ventricular tachycardia, eventually leading to sudden cardiac death. All drugs must be therefore evaluated for their potential to delay cardiac repolarization in clinical trials. However, measuring the QT-interval with the required statistical accuracy is difficult. We have therefore developed a solution based on our intelligent analysis algorithm, which allows managing and analyzing the results of QT/QTc studies centrally, with an efficient mechanism to proof results by a confirming cardiologist. Keywords— Central ECG analysis and interpretation, QT/QTc-study, delay of cardiac repolarization, sudden cardiac death, clinical trial solution, electronic data capturing

I. INTRODUCTION An undesirable property of some drugs is their potential for secondary effects, which may cause critical adverse reactions by patients. Some drugs have the ability to delay cardiac repolarization, which can be measured as prolongation of the QT (QTc) interval in the electrocardiogram (ECG). A delay in cardiac repolarization favors the development of arrhythmias, e.g., Torsade de Pointes (TdP). A main feature of TdP is a significantly prolonged QT interval – unfortunately, TdP may degenerate into ventricular fibrillation leading to sudden cardiac death. The Food and Drug Administration (FDA) of the United States and the European Medicines Agency therefore require the clinical evaluation of QT/QTc interval prolongation and pro-arrhythmic potential even for nonantiarrhythmic drugs. But the threshold level of regulatory concern is around 5 ms as evidenced by an upper bound of the 95% confidence interval around the mean effect on QTc of 10 ms [1] – this requires very accurate acquisition and analysis of the 12 lead surface ECG. Up to now, most central ECG laboratories work with multiple ECG acquisition devices (with varying, usually not validated accuracy!) and involve human readers for the analysis of the ECG intervals. However, it is well known that the inter- and intra-reader variability is usually high. This may lead to a reduced statistical accuracy and reduce or, in worst case, even lead to a

total loss of comparability of results within and across clinical trials. We have therefore developed, implemented and patented a new intelligent internet based concept for conducting and analysing QT/QTc studies. II. INTELLIGENT CLINICAL TRIAL SOLUTION Our concept for high quality, highly accurate ECG analysis for clinical trials is based on four main components [2]: 1. A high quality, low cost 12 lead PC-based ECG acquisition device. This is applied at the trial site and connected via internet. 2. A graphical user interface for doctors, trial managers core lab personnel and confirming cardiologists, which can be accessed via any internet browser and contains all processes needed in a central ECG core lab. 3. A data base, where all patient data, all ECGs and other diagnostic methods (e.g., blood pressure, SPO2) can be stored and used for further analysis (electronic data capturing). 4. An intelligent algorithm to automatically analyze incoming ECGs. For this we apply our well known Hannover ECG System HES® with specific improvements in measuring the QT/QTc interval.

Fig. 1 Clinical Trial Solution.

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Intelligent Internet Based, High Quality ECG Analysis for Clinical Trials

The process of collecting and analyzing ECGs is given in figure 1. First, the 12 lead ECGs are collected at the trial site using a PC-based acquisition device. Simple acquisition software running on the PC contains an automatic secure upload function provided that the PC is connected to the internet. The ECG data (with patient ID) are then automatically uploaded to the central data server, where the 12 lead ECG is measured and interpreted with our HES-algorithm in real time. All data are then automatically stored in the data base and can be reviewed either by the trial site itself or a confirming cardiologist. Before official approval by a human reader, the ECG is marked “red” as “unconfirmed”. Usually, the confirming cardiologist will then do an overreading of the ECG (using, e.g., a caliper tool to correct wave points, if necessary) – the ECG is confirmed and marked “green”. All steps performed by any user are tracked and can be followed by the trial manager (administrator). The main advantage of this procedure is the fact that all ECGs are in principle analyzed with the same mathematical method – without the inevitable variability of humans. Statistical accuracy is therefore significantly higher, which is essential to detect, e.g., the small drug induced prolongations of the QT/QTc interval. Once a clinical trial is finished, all data can be exported to other data bases or statistical analysis tools. For the ECG, we also offer the FDA-XML format to submit directly to the FDA ECG warehouse. The access to the central server platform can be strictly limited – the administrator or trial managers can set-up accounts for every user of the platform and thereby prevent any unintended use of data. III. USER MODEL OF CLINICIAL TRIAL SOLUTION The clinical trial solution maps typical processes of a central ECG core lab onto internet based user models and processes. The platform is designed to deal with multicentre studies and can be accessed via any internet browser without the need of any locally installed software. We have implemented three main account types: 1. Trial Manager (administrator): The trial manager is responsible for the complete set-up of a clinical trial – this means creating the required logins, uploading patient data and setting the relationship between patients, physicians at trial sites, and confirming cardiologists. The trial manager also controls all settings, e.g., whether patient names are shown or only patient ID’s for blind studies. The trial manager is also responsible for data export, once a study is finished for further analysis and or data upload to the FDA.

23

2. Physicians at trial sites: The physicians record the ECG and perform other examinations (e.g., blood pressure, temperature, weight) at the trial site. They have a password protected log in and upload all data to the central server. Physicians may also add new patients to the trial. They can access all saved data for their patients (electronic patient record), to compare, e.g., an ECG before and after medication. 3. Confirming cardiologists: Confirming cardiologists log into the platform via a web-browser as well. The confirming cardiologist is assigned to a physician at the trial site and automatically receives all ECGs for overreading from the assigned physician. Once an ECG is confirmed, the physician gets status “green” for the respective ECG. All changes / comments of the cardiologist are tracked – once confirmed, an ECG cannot be changed anymore. All patient records (examinations) and actions performed on the platform receive an automatic stamp of time and date, which cannot be changed by physicians or confirming cardiologists and is saved in the data base. This allows a seamless tracking of every “click” performed on the platform to prevent from manipulation or loss of information. IV. INTELLIGENT ALGORITHMS FOR ECG-ANALYSIS The core of our clinical trial solution is the HES algorithm for the analysis of 12 lead resting ECGs or Holter ECGs with 2, 3, or 12 leads. The HES program was originally developed in 1971 and since then continuously improved together with cardiologists. We follow a complex strategy to analyze the ECG signals: Starting from the unfiltered signals, we locate all beats in the first step. The second step comprises typing of beats – we distinguish several types of normal beats, 20 different types of premature ventricular contractions (PVC’s), supra ventricular premature contractions (SPVC’s), aberrant beats or artifacts. Only with the normal beats, applying a mathematical complex averaging, we obtain the representative cycle for the ECG under evaluation. With this technique we get rid of most noise within the signal and can accurately locate all relevant wave points for the P-wave, the QRScomplex and the T-wave. Using the accurate wave point we then calculate all relevant intervals and amplitudes, like, e.g., the QT interval. Within the HES algorithm we apply a multi-variant statistical analysis for the interpretation of ECGs; with this method, the algorithm can be trained to very high sensitivity or specificity for detecting certain diseases within a given population.

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T.K. Zywietz, R. Fischer

Since the QT interval has an inverse relationship to heart rate, the measured QT intervals must be corrected for heart rate. Various correction formulae have been developed, of which Bazett’s and Fridericia’s corrections are the most common. The best correction formulae are still a subject of controversy. We have therefore implemented most of the currently available approaches [3, 4, 5, 6]: 1. Fridericia’s correction 2. Bazett’s correction 3. Framingham correction 4. Rautaharju correction The QTc-formulae should be applied in parallel to identify the optimal formula for the population under testing. In most clinical trials, pre-cordial leads and lead II are used to measure the QT/QTc interval. In our HES algorithm, we apply ALL available leads for the analysis of the intervals, increasing the accuracy of detecting Q and T-end. The algorithm also analyzes morphological changes of the T-wave and the ST-T segment. The performance of the HES program was tested against the 100 biological 12-lead ECGs of the CTS/CSE data base as required by the EN / IEC 60601-2-51 norm (Table HH.2). The performance of the algorithm was measured for each of the 100 ECGs and compared with the reference. The reference of the CSE data base is provided by the norm as average of the measurement of 10 internationally recognized cardiologists [7]. The summarized results over all ECGs are provided in Tab. 1. Table 1:

Median Mean Std. Deviation Std. Error Std. Error (%)

Performance of HES algorithm against CSE data base PQ-Interval Reference Difference 158,0 0,0 162,8 -0,6 27,6 5,9 0,6 0,4

QT Interval Reference Difference 398,0 2,0 400,6 0,2 43,8 8,9 0,9 0,2

The deviation of the HES algorithm is very small. Based on theses results we conclude that the accuracy of absolute wave duration and interval measurements is sufficient to be applied to QT/QTc studies. More detailed results on the performance of HES are available on request from the authors. V. INTEROPERABILITY – THE OLD PROBLEM A pre-requisite for a central ECG analysis platform is the interoperability with the acquisition devices. Unfortunately, many manufactures still use proprietary formats. However, for computer assisted electrocardiography a specific stan-

dard (“SCP-ECG”) was developed and approved by CEN (Current version EN 1064:2005 [8]). This standard specifies the interchange format and a messaging procedure for ECG cart-to-host communication and for retrieval of SCP-ECG records from the host (to the ECG cart). The SCP standard was implemented by a couple of European and American manufacturers and is also used within our central platform. Practical experience during implementation and in the field revealed its superior performance, e.g., for telemetric applications as well as for data volume effective storage and retrieval (e.g., in the OEDIPE project [9]). Despite the approved standard, most ECG system manufactures still do not comply completely with the standard. A central ECG laboratory must therefore check compliance of the incoming ECG records to avoid critical errors. We have developed a content and format checker for the SCP standard [10, 11] and have implemented it into our central ECG platform. All incoming ECGs are format checked, and only completely compliant records are accepted. Aditionally, other formats, such as the FDA-XML format are implemented as well, especially for the export of ECG data. VI. ADVANTAGES OF CONSISTENT ECG ANALYSIS Detecting the small drug induced changes within the ECG in general and specifically for the QT/QTc interval of 5 ms is, as mentioned above, a considerable task. Studies such as the European CSE project [8] have revealed the problem of variability of ECG measurement and interpretation between different human readers and even the variability of ONE human reader, if the same ECG is analyzed on different days. Although in difficult ECG cases experienced cardiologists demonstrate the superior capability of humans in pattern recognition, the automatic analysis using high performance algorithms reveals a number of advantages: 1. The consistency of analysis results. If the recording is correct and if the noise level is low enough, the computer does not get tired and will provide always the same results for the same ECG. 2. Especially for clinical trials the computer delivers consistent, statistically meaningful results. This is very helpful for serial ECG comparison, e.g., if changes are to be monitored after medication. 3. For each ECG analyzed by the computer, objective, human independent measurements are obtained which can be used in long term observation and serial comparison. 4. Usually the diagnostic criteria applied by the programs are carefully selected and not based upon just a single “ECG school”.

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Intelligent Internet Based, High Quality ECG Analysis for Clinical Trials

5. Documentation of ECG analysis results is provided automatically (and always in the same format) and the physician does not need to dictate results for typing and writing a doctor’s letter. 6. All ECG and patient data are automatically saved within a data base and can be statistically analyzed over the complete patient population. 7. In case of “wrong” analysis by the algorithm, applying the internet based trial solution, confirming cardiologists can easily correct measurements and interpretations of ECGs using a consistent approach. However, using automatic analysis without the necessary care is dangerous: Many of the available automatic algorithms do not have the required accuracy to apply them for clinical trials. Algorithms should fulfill at least the international norm IEC 60601-2-51, clause 50.101 and 50.102 (measurement and interpretation accuracy). Clinical trial sponsors should spent extreme care to the quality of applied ECG recorders and analysis software. VII. CONCLUSIONS AND PERSPECTIVES The HES Clinical Trial Solution is a very accurate and efficient tool to support thorough QT/QTc studies for the approval of drugs. The centrally applied algorithms for 12 lead ECG analysis lead to a higher statistical accuracy of interval measurements and avoid inter- and intra-reader variability during clinical trials. The efficiency of a central ECG laboratory can be significantly enhanced applying modern internet based technologies to support or completely automating typical processes of QT/QTc studies. The platform will be systematically enhanced to allow also electronic data capturing for other diagnostic methods such as, e.g., echo cardiograms or magnetic resonance imaging.

25

ACKNOWLEDGMENT We thank Andreas Bulling, Eugen Tarita and Andreas Krüger for implementing a prototype of our internet based ECG management platform.

REFERENCES 1. 2. 3. 4. 5.

ICH E14, EMEA, European Medicines Ageny, 2005. Bulling, A, Master Thesis, Karlsruhe, Germany, 2006. Bazett HC, Heart 1920; 7:353-370. Fridericia, LS, Acta Med Scand, 1920. Sagie A, Larson MG, Goldberg RJ, et al., Am J Cardiol 1992;70:797-801. 6. Rautaharju, PM, Warren JW, Calhoun HP, J. Electrocardiol. 1990, 23 Suppl:111-7. PMID 2090728. 7. CSE study, New England J. of Medicine, Vol. 325, 25, page 1769, 1991. 8. Health informatics-Standard communication protocol - Computer- assisted electrocardiography. CEN European Standard EN 1064, 2005. 9. OEDIPE - AIM 2026, Open European Data Interchange and Processing for Computerised Cardiography, 1993-08-19, Deliverable 7. 10. Fischer R, Zywietz C, Widiger B, IFMBE Proc. vol 6, MEDICON and Health Telematics, Ischia (Naples), Italy, 2004, paper 225. 11. Fischer R, Chiarugi F, Zywietz TK, Enhanced Integrated Format and Content Checking for Processing of SCP ECG Records, Computers in Cardiology 2006, Vol. 33, pp 413-416. Address of the corresponding author: Author: Institute: Street: City: Country: Email:

Dr. Tosja K. Zywietz Biosigna – Medical Diagnostics Lindwurmstrasse 109 80337 Munich Germany [email protected]

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Modelling effects of Sotalol on Action Potential morphology using a novel Markov model of the HERG channel T.P. Brennan1, M. Fink2, B. Rodriguez3, L.T. Tarassenko1 1

Department of Engineering Science, University of Oxford, United Kingdom 2 Department of Physiology, University of Oxford, United Kingdom 3 Computing Laboratory, University of Oxford, United Kingdom

Abstract— In this paper, we present a simulation study of the effects of Sotalol, a known anti-arrhythmic drug, on the rapid delayed rectifier potassium current (IKr). The current is encoded by the Human Ether-a-go-go Related Gene (HERG), which plays a major role in repolarization in mammalian ventricles. HERG is also the target of class III anti-arrhythmic drugs, such as Sotalol. Due to its unique structure and electrophysiological qualities, non-cardiac drugs readily bind with residues inside HERG's intracellular cavity. A novel Markov model was developed to model Sotalol's interaction with HERG. The model was validated using experimental data from HERG expressed in Human Embryonic Kidney (HEK) cells and integrated into the ten Tusscher (2006) human ventricular cell model. The simulation results show that an increase in Sotalol concentration decreases the overall conductance of IKr over time, resulting in prolongation of the action potential duration. This effect is larger in mid-myocardial than in endocardial and epicardial cells. Therefore, Sotalol-induced effects on cardiac repolarization may result in enhanced transmural dispersion of repolarization in the ventricles, and also in changes in the T wave.

tential duration (APD), and are responsible for LQTS2, the most common phenotype of LQTS [2]. Furthermore, drugs such as Sotalol that inhibit IKr result in prolongation of the APD and the QT interval, and this might result in an increased propensity to develop Torsades de Pointes, a potentially fatal ventricular tachycardia historically linked with LQTS. However, changes in ventricular repolarization and in the morphology of the T wave caused by drug-induced IKr inhibition are not well investigated. This simulation study aims to investigate changes in action potential (AP) morphology caused by Sotalol binding to human IKr. To do so, a Markov model of the human IKr was adapted to represent the binding kinetics of Sotalol. The model was incorporated in the ten Tusscher human AP model [17] and changes in IKr and AP morphology with varying drug concentration were investigated. The results of this study represent a first step towards understanding the effect that drug inhibition of HERG has on the morphology of the T-wave in the human electrocardiogram.

Keywords— HERG, Sotalol, Markov model, Computer Simulation.

I. INTRODUCTION Drug-induced effects on cardiac behaviour are generally quantified as changes in the QT interval of the surface electrocardiogram. However, alternative biomarkers such as the T wave morphology might be more efficient in assessing the arrhythmic potential of new pharmaceuticals. Although the origins of T-wave morphology remain still incompletely understood, they are linked to cardiac repolarization. Therefore, alterations in cardiac repolarization due to drug-induced effects or genetic mutations are likely to result in changes in the T-wave morphology. Thus studies of congenital Long QT Syndrome (LQTS) identified distinct T-wave morphologies in patients with different phenotypes [1]. The rapid inward rectifier potassium current (IKr), encoded by the Human Ether-a-go-go Related Gene (HERG) is known to play an important role in cardiac repolarization. Thus, mutations of HERG result in alterations in action po-

II. METHODS A. Markov model of IKr inhibition by Sotalol The processes of Sotalol binding to IKr, channel activation and inactivation are not independent [3], and thus cannot be modelled using a Hodgkin-Huxley ion channel formulation as in the ten Tusscher model [17]. Therefore, in this study we used a continuous-time Markov model of IKr, which has the ability to represent ion channel gating characteristics and ligand-binding [4]. The Markov model is defined by a set of state transition rates, which are the elements of the transition rate matrix Q. Each element qij defines the transition rate between any two states i and j [5]. In the presence of a large number of identical channels the change in the fraction of channels can be expressed in terms of state occupancy probability and state transition rates [6]. The probability that the channel is in the open state O is then included into the ohmic equation for IKr, i.e. IKr = gKr·O·(Vm – Ekr), where gKr is the conductance of the channel, Vm is the transmembrane potential and Ekr is the Nernst potential for potassium.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 50–53, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

Modelling effects of Sotalol on Action Potential morphology using a novel Markov model of the HERG channel

A number of previous Markov models of IKr have been developed for various species; guinea-pig [7], rabbit [8], HERG expressed in Xenopus Oocytes [9], Chinese Hamster Ovary (CHO) cells [10], Human Embryonic Kidney (HEK) cells [11], and combined data from HEK cells and human ventricular myocytes [12]. Since we are interested in investigating drug-induced changes in cardiac repolarization in human, here we used a modified version of the human IKr model proposed by Fink et al. [12]. A recent review by Sanguinetti and Tristani-Firouzi highlighted the molecular mechanisms of drug interaction with IKr. Seven residues inside the channel's intracellular cavity were identified that readily bind with drug compounds [13]. In particular, Sotalol binds to the open or activated state of IKr gaining access to the receptor from the intracellular side. The bond is then stabilized by channel inactivation. The Sotalol molecule is trapped inside the channel cavity until the channel returns to an activated state [14]. The proposed state diagram for the HERG-Sotalol Markov model for drug inhibition of IKr is shown in Fig. 1. The parameters for the transition rates (see Appendix) between closed, open, and inactivated states were taken from [12] and were, as a first approximation, assumed not to be affected by the binding of the drug. In order to model the inhibition of IKr by Sotalol, a blocked state D* was introduced and the transition rates between the open O state and the blocked state D* were given in terms of the association (k1) and dissociation (k2) rate constants as well as the concentration of Sotalol ([L]). The IKr model was run at near-physiological temperatures (35o C). Initial intracellular and extracellular potassium concentrations, [K+]i and [K+]o, were set to 130 mM and 4 mM in line with experimental data. [K+]o effects on gKr were modeled using a square root dependence as previously described [12]. All simulations were carried out using MATLABTM on Intel Pentium 4TM. The dissociation constant (Kd) is the ratio of k1 over k2. The binding kinetics of Sotalol and HERG have been investigated in competitive binding studies using HERG ex-

51

pressed in HEK cells. Kd was found to be 49 ± 9 μM in CHO cells at 37o C [15]. The association and dissociation rate constants could not be found in the literature; therefore k1 and k2 were determined by fitting experimental data using MATLABTM. The assumption was made that the kinetic rates were independent of membrane potential. B. Stimulation protocols Sotalol inhibition of IKr depends on the voltage step-pulse protocol in the context of in vitro experiments. Therefore, to facilitate comparison with experimental data, voltage step protocols used in [16] were used in the simulations to facilitate comparison with the experimental data for model validation. In brief, two protocols were used: 1. Step ramp consisting of a conditioning step (+20 mV amplitude, 1s duration) followed by a repolarizing test ramp (+20 to -80 mV at -0.5 V/s) repeated at 5s intervals from a holding potential of -80 mV. 2. Step pulse (2s) consisting of a pre-pulse to +20 mV for 2s followed by a test pulse to -50 mV for 2s repeated every 10s intervals from a holding potential of -80 mV. C. Human ventricular action potential model The effects of Sotalol on AP morphology were analyzed using a modified version of the ten Tusscher (2006) model of the human ventricular AP [17]. Our new Markov model of IKr including Sotalol binding, replaced the rapid inward rectifier potassium current in the ten Tusscher model. Endocardial, epicardial and mid-myocardial AP were simulated using the model, considering differences in the conductance of IKs (slow-delayed rectifier potassium current) and of the transient outward potassium current (Ito) across the ventricular wall, as previously described [17, 18]. All simulations were carried out using MATLABTM on Intel Pentium 4TM. III. RESULTS A. Model validation

Fig. 1 Schematic of the Markov model of the HERG ion channel including effects of Sotalol biding at the open state. [L] is the Sotalol concentration. States depicted with an asterisk(*) imply states with Sotalol block. C refers to closed state, O to open state, I to inactivated state and D to blocked state.

Fig. 2 illustrates the change in IKr caused by 300 μM of Sotalol at 35o C using a step-ramp pulse protocol. The HERG-Sotalol Markov model was compared to experimental data from HERG expressed in HEK cells under the same conditions [16]. Using fminsearch, which implements the Nelder-Meade algorithm to minimize a cost function, the association rate constant of Sotalol was determined to be k1 = 0.005 μM-1.s-1 and the dissociation rate constant k2 = 0.00125 s-1.

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T.P. Brennan, M. Fink, B. Rodriguez, L.T. Tarassenko

tion protocol or to a leak current. This dynamic was incorporated for this simulation only by linearly decreasing the driving force by 0.1% per second. C. Effects of Sotalol on Action Potential

Fig. 2 Sotalol inhibition of IKr simulated using a step-ramp pulse protocol and 300 μM Sotalol at 35o C. Steady state channel responses in control i.e. without drug (solid lines) and in the presence of 300 μM Sotalol (dashed lines) are shown. for (A) Experimental data from HERG in HEK cells from [16] Fig. 6A and (B) Model response. In both experiments and simulations, Sotalol-induced effects on IKr result in a reduction of both steady state and peak IKr current. Importantly, the simulated ratio between control and blocked current for steady state and peak levels is similar to experimental observations (Fig. 2). Differences in IKr re-activation levels between the experimental data and model for the control shown in Fig.2 might be due to differences in phosphorilation of the channel and ion concentrations in the intra- and extracellular medium. However, this does not affect the equivalence of the drug inhibitory effects between model and experiment. . B. Sotalol inhibition of IKr Fig. 3 illustrates the decrease in peak normalized IKr caused by 500 μM Sotalol in the experiments from [16] (dotted) and in the simulation results (solid). The slope after 200 s could only be explained by a decrease in the driving force (Vm-EK) over time due to the prolonged depolariza-

Fig. 4 shows the time course of Vm and IKr during an AP in control (solid) and in the presence of 300 μM Sotalol (dotted), for endocardial (top), mid-myocardial (middle) and epicardial cells, obtained from the computer simulations. For the three cell types, Sotalol results in a significant prolongation of the APD, which is more pronounced in midmyocardial cells than in the endo- and epicardial cells. This is due to the fact that IKs density is lower in the midmyocardium than in endo- and epicardium, and therefore IKr inhibition results in a slower repolarization in the midmyocardial cells than in the other two cell types. These results suggest that transmural differences in APD might be enhanced in the presence of Sotalol. IV. CONCLUSIONS This study investigates the electrophysiological changes in human ventricular myocytes caused by Sotalol-induced inhibition of IKr. A human ventricular Markov model of membrane kinetics was developed to include the interactions between HERG and Sotalol. The model was validated using experimental data of HERG expressed in HEK cells at near-physiological temperatures. Simulated IKr showed Sotalol-induced inhibition in line with experimental data. Simulation results also show that an increase in Sotalol concentration results in prolongation of the APD in endocardial, epicardial and mid-myocardial cells. The prolongation was larger in mid-myocardial than in epicardial and endocardial cells. Therefore, an increase in transmural dispersion of repolarization in the ventricles is expected in the presence of Sotalol, which could be pro-arrhythmogenic [7], and could lead to changes in the T wave morphology.

APPENDIX Table 1

Parameters for transition rates of IKr Markov Variable

ΔS

ΔH

C1→C2, C1*→C2*

α1

-100.36

66797

2.992e-004

C2→C1, C2*→C1*

β1

-188.89

40486

-1.611e-003

C2→C3, C2*→C3*

α2

-113.57

66797

0.0

C3→C2, C3*→C2*

β2

-193.00

40486

0.0

C3→O, C3*→D*

α3

-118.96

66797

9.751e-004

O→C3, D*→C3*

β4

-241.90

40486

-1.066e-003

O→ I, D*→I*

α5

-53.12

79619

5.958e-004

I→O, I*→D*

β5

-52.74

85688

-8.335e-004

Transitions

Fig. 3 Time course of HERG inhibition by Sotalol given as normalized peak of IKr current amplitude when applying a voltage clamp step pulse (2s) protocol and 500 μM Sotalol at 35o C. The solid line denotes the model output. Experimental data of IKr current from HERG expressed in HEK cells from [16] Fig. 4D is shown as dotted line.

Z

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Modelling effects of Sotalol on Action Potential morphology using a novel Markov model of the HERG channel

4. 5. 6.

7. 8.

9. 10. 11.

12. 13. 14. 15.

Fig. 4 Simulated time course of action potential (AP) and IKr in epi-, endo- and mid-myocardial cells in control (solid lines) and in the presence of block by 300 μM Sotalol.

REFERENCES 1.

2. 3.

Zhang L et. al. (2000), 'Spectrum of ST-T-Wave Patterns and Repolarization Parameters in Congenital Long-QT Syndrome: ECG Findings Identify Genotypes', Circulation 102(23), 2849-2855. Curran ME et. al. (1995), 'A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome.', Cell 80(5), 795-803. Kiehn J, Lacerda AE, Wible B & Brown AM (1996), 'Molecular physiology and pharmacology of HERG. Single-

16. 17. 18.

53

channel currents and block by dofetilide', Circulation 94(10), 2572-2579. Colquhoun D & Hawkes AG (1981), 'On the stochastic properties of single ion channels', Proc R Soc Lond B Biol Sci 211(1183), 205-235. Horn R & Lange K (1983), 'Estimating kinetic constants from single channel data.', Biophys. J. 43(2), 207-223. Destexhe A, Mainen ZF & Sejnowski TJ (1994), 'Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism', Journal of Computational Neuroscience V1(3), 195-230. Clancy CE & Rudy Y (2001), 'Cellular consequences of HERG mutations in the long QT syndrome: precursors to sudden cardiac death.', Cardiovasc Res 50(2), 301-313. Oehmen CS, Giles WR & Demir SS (2002), 'Mathematical model of the rapidly activating delayed rectifier potassium current I(Kr) in rabbit sinoatrial node.', J Cardiovasc Electrophysiol 13(11), 1131-1140. Weerapura M et. al. (2000), 'State-dependent barium block of wild-type and inactivation-deficient HERG channels in Xenopus oocytes.', J Physiol 526, 265-278. Lu Y et. al. (2001), 'Effects of premature stimulation on HERG K(+) channels.', J Physiol 537(Pt 3), 843-851. Mazhari R et. al. (2001), 'Molecular interactions between two long-QT syndrome gene products, HERG and KCNE2, rationalized by in vitro and in silico analysis.', Circ Res 89(1), 33-38. Fink M et. al. (2007), 'Contributions of HERG K+ current to repolarization of the human ventricular action potential', Prog Biophys Mol Biol [In press]. Sanguinetti MC & Tristani-Firouzi M (2006), 'hERG potassium channels and cardiac arrhythmia', Nature 440, 463-469. Mitcheson JS, Chen J & Sanguinetti MC (2000), 'Trapping of a methanesulfonanilide by closure of the HERG potassium channel activation gate', J Gen Physiol 115(3), 229-240. Fiset C, Feng ZP et. al. (1996), ' [H+]Dofetilide binding: biological models that manifest solely the high or the low affinity binding site' J Moll Cell Cardiol 28, 1085-1096. GE, Kirsch et. al. (2004), 'Variability in the measurement of hERG potassium channel inhibition: effects of temperature and stimulus pattern', J Pharm Tox Meth 50, 93-101. ten Tusscher KHW et. al. (2004), 'A model for human ventricular tissue', Am J Physiol Heart Circ Physiol 286, H1573H1589. Gima K & Rudy Y (2002), 'Ionic current basis of electrocardiographic waveforms: a model study', Circ Res 90, 889-896. Address of the corresponding author: Thomas Brennan Department of Engineering Science, University of Oxford Parks Road, Oxford United Kingdom OX1 3PG Email: [email protected]

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Neural Networks Based Approach to remove Baseline drift in Biomedical Signals J. Mateo1, C. Sanchez1, R. Alcaraz1, C. Vaya1 and J. J. Rieta2 1

Innovation in Bioengineering Research Group, Castilla La Mancha University, Cuenca, Spain 2 Biomedical Synergy, Valencia University of Technology, Gandia (Valencia), Spain

Abstract— Nowadays there exist different approaches to cancel out noise effect and baseline drift in biomedical signals. However, none of them can be considered as completely satisfactory. In this work an artificial neural network (ANN) based approach to cancel out baseline drift in electrocardiogram signals is presented. The system is based on a grown ANN allowing to optimize both the hidden layer number of nodes and the coefficient matrixes. These matrixes are optimized following the simultaneous perturbation algorithm, offering much lower computational cost that the traditional back propagation algorithm. The proposed methodology has been compared with traditional baseline reduction methods (FIR, Wavelet-based and Adaptive LMS filtering) making use of cross correlation, signal to interference ratio and signal to noise ratio indexes. Obtained results show that the ANN-based approach performs better, with respect to baseline drift reduction and signal distortion at filter output, than traditional methods. Keywords—Cancellation Noise, Baseline, Neural Networks and Madeline.

I. INTRODUCTION The Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart that offers information about the state of the cardiac muscle. With Filtering techniques applied in ECG it would be possible to improve the diagnosis of some diseases of the heart and diverse pathologies [1]. The bandwidth of the acquiring system is usually from the 0.05Hz to 100Hz with almost linear res-ponse, causing no distortion of the pulse waveform. How-ever, distortion may arise from the movement of the subject respiration. The frequency components of the respiration are usual-ly below 0.8Hz. The motion artefacts are characterized by lowfrequency components too. There are some approaches on handling baseline wander of ECG, MEG and impedance cardiogram [2]. Some researchers attempt to suppress baseline wander with high pass filter, which introduces nonline-ar phase distortion and the key points displacement. To preserve phase information, the symmetric FIR digital filter is designed, but it does very little to attenuate low frequency baseline wander [3]. Some people can have the rapid pulse, moderate pulse or slow pulse. What is more, the frequency of respiration is also various. In consequence, the pulse and its baseline drift vary with

different people. Sörnmo used the time-varying filter to remove the baseline wander of ECG [4]. But the algorithm is complex, level dependent and rhythm dependent. Laguna [5] has used adaptive filter to remove the ECG baseline wander. Similarly, adaptive filters and Wiener filters have been of limited value in the absence of prior knowledge of the physiological signal and its baseline drift [6] and Wavelets [7-8]. This method has not been applied in the cancellation of baseline noise in ECG signals yet. This system has two important advantages: to reduce the processing time, to provoke low signal distortion, to reduce diverse noise types and, in addition, it can be applied to a wide range of biomedical signals. II. MATERIALS The electrocardiography treated signals validation requires a set of signals which will have to cover the pathologies, leads, etc in real situations. For this study, two types of signals were used: real recordings from the PhysioNet Database [9], and synthetic signals. Table 1: Signals used for the study Synthetic Real Real+noise(Ecgsyn)

Nº Of registers 200 565 550

Time (seg) 1049 106 106

550 recordings with different pathologies have been obtained from PhysioNet with different types of QRS morphologies. These recordings were sampled at a frequency of 360 Hz and, later, they were up sampled to obtain a frequency of 1kHz. Synthetic signals with different noises have been generated making use of the ECGSyn software [9]. White noise, muscular and artefacts are included in these registers. The sampling frequency used is 1kHz. III. METHODS A. Neural Networks The neuronal perceptron multilayer network(MLP) using the algorithm of backpropagation has been applied to di-

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Neural Networks Based Approach to remove Baseline drift in Biomedical Signals

verse practical problems [10]. Perceptron multilayers method consists of at least three layers: A input layer, one or more hidden layers and an exit layer. A way to consider the optimal number of nodes in the hidden layer is to stop the training after a certain number of iterations and to determine how many signals were filtered with the present number of neurons used in the hidden layer. If the result of this test is not satisfactory it will add one or more neurons in the hidden layer to improve the performance of the network. In these cases the network must be completely trained [10]. An attractive alternative is the development of increasing networks in which nodes are added in the hidden layer in systematic form during the learning process. With this idea diverse structures have been proposed such as the cascade-correlation network [11], as neural networks [12-14]. This article proposes an increasing neuronal network in which it is necessary to optimize the nodes number of the hidden layer to optimize and to give solution to two problems in the MLP. In the proposed system the number of nodes is added. The network is optimized adding a node to the hidden layer after a number of iterations. The weights that connect the input layer with the hidden layer are conserved. On the other hand to reduce the computational complexity, an algorithm based on the simultaneous perturbation method is used [10][15-18]. B. Proposed System The proposed system consists initially of a simple structure similar to the neural network ADALINE (ADAptive LINear Element), which is used like initial structure because this one is simple and easy to optimize using the algorithm of square minimums average, LMS [12]. It is had initially an input layer layer, one hidden layers (with three neurons) and an exit layer, where they will be added neurons in the intermediate layer. When network has converged, if the operation obtained by the system is not the required one, adds a neuron in the hidden layer, as in the Figure 1. In this case the weights that connect the input layer with the nodes of the hidden layer are congealed, these are been previously trained. The weights that connect the hidden layer with the exit layer are adapted, as well as the weights that connect the input layer with the neuron added in the intermediate layer, as it is shown in Figure 1. Once trained the network, it returns to evaluate its operation. If it is not correct, a new neuron will be added in the hidden layer. Next, the weights that connect the hidden layer with the exit, as well as the weights connect the input layer with the new neuron added in the intermediate layer

91

Input

Output

1

+

Fs

+

Fs

Y1

X1

+

Fs

+

Fs

Y2

Xn

+

Fs

+

Fs

Ym

+

Fs

W

V

Hidden layer

Fig. 1 Proposed Neuronal Network with one neuron in the hidden layer. The black coefficients are constants

are trained. This procedure is repeated until obtaining the wished operation. This new structure has a special characteristic: it grows while it learns. It means that the added neurons in the hidden layer adapted weights, whereas the weights of the input layer conserves the learning of the obtained network. This mechanism, although sometimes could produce neuronal networks with an sub-optimal number of neurons in the intermediate layer, allows to consider of approximate way the size of the required network to carry out a certain operation without having to train the neuronal network completely whenever a neuron in the hidden layer is added. Thus, the neurons that have been added allow a good operation of the network in general. In all the stages the neuronal network is adapted using the method of simultaneous perturbation which has been proven and has shown good results. C. Learning Algorithm using Simultaneous Perturbation In [16-17] it is possible to introduce the simultaneous perturbation method. Other authors [15] have also reported results of similar methods. To adapt the weights of the system it is necessary to consider the gradient of the error function, this is:

∇≅

∂J ( w) ∂w

(1)

Defining the error function like: J ( w) =

1 ( y − y d )2 2

(2)

where;

ε = (y − yd )

(3)

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Using the equation 3 it is possible to measure the error between the present exit and the wished exit. On the other hand, the approach of differences is a procedure known to obtain the derived from a function, so this approach to reduce the complexity can be used [10]. The vector w is defined as [16], c is a perturbation added to the i component: The neural network exit, Y, is a function of the vector of weights: ∂J ( w) ∂w i

≈

f (Y ( w i )) − f (Y ( w)) c i

w Ä for all the network operations. It is due to evaluate JÃ weights to obtain the amount modified for all the weights. Y (Wt + Ct , Vt + Dt ) − Y (Wt ,Vt ) cti

Frequency

0.1 Hz

SNR (dB) Error %

0.8

-5.2

-8.7

0.8

-5.2

-8.7

0.01

0.03

0.3

0.1

0.03

4.9

Neural

0.1

0.3

0.5

0.2

0.3

0.5

Frequency

(5)

So a good general operation for all the adapted weights cannot be hoped. In order to eliminate this difficulty the simultaneous perturbation was introduced in which all the network weights are perturbed simultaneously [17] as it is indicated in the equation 5. The projections for the convergence of the algorithm of simultaneous perturbation have been demonstrated [16].

Error %

0.4 Hz

0.6 Hz

0.8

-5.2

-8.7

6.8

0.8

-2.8

Wavelet

3.8

19

112

23

54

134

Neural

0.2

0.4

0.7

0.8

1.5

3.7

The aim is to find the best trade-off between information preservation and baseline wander removal. This distortion does not influence the efficient of pulse diagnosis and pulse pattern recognition. Especially, the actual pulse waveform baseline frequency contents are rich. Thus, it is prompted the neural filter for the removal of the pulse baseline. ⎛ signal ⎞ ⎟ SNR = 20 ∗ log 10⎜ ⎜ noise ⎟ ⎝ ⎠ error =

(6)

clean signal − filtered signal

(7)

clean signal

⎛ ⎜ E{ xin − x SIR = 20 log⎜ ⎜ E{ xout − x ⎝

IV. RESULTS For further research of the Neural Networks filter, it is simulated the clean pulse signal and corrupted with different baselines. It was compared the performance of the neural network approach with the standard filtering techniques. The Butterworth high pass digital filter is nonlinear phase filter, so the pulse waveform can be distorted. Bessel filter phase is perfect but its amplitude is very weak. Thus it is designed the traditional linear-phase FIR filter using least squares error minimization. Table 2 lists the error of the Wavelet and the Neural Networks during different signal to noise ratio (SNR) of baseline’s frequency is 0.1, 0.2, 0.4, 0.6 Hz respectively. When the baseline’s frequency is less than 0.2Hz, the Wavelets method is satisfied. To the Neural filter, when the baseline’s frequency is less than 0.6Hz its filter results are satisfied for the pulse parameters computing and analysis and the error is 3.7% even when the SNR is -2.8db.

0.2 Hz

Wavelet

SNR (dB)

(4)

Nevertheless the above idea which is very simple, needs more

Δwti =

Table 2 Comparison of errors of Wavelet and Neural Networks

2 2

⎞ ⎟ ⎟ ⎟ ⎠

(8)

Table 3 Obtained results of the cross correlation and SIR of baseline, average values Frequency

Synthetic

Real

SIR

FIR LMS Wavelet Neural Networks

0.92 ± 0.03 0.63 ± 0.32 0.94 ± 0.02 0.97 ± 0.02

0.91 ± 0.03 0.60 ± 0.35 0.93 ± 0.02 0.96 ± 0.02

13.2 ± 0.3 5.8 ± 2.23 16.2 ± 0.3 19.2 ± 0.3

It is token measures to show the cross correlation exit and input results, table 3. On the other hand it is need to measu-re the (SIR), this last way for synthetic signals. Equation 8, shows SIR expression where xin shows the input to the system, xout the exit and x the original registry without noise.

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Neural Networks Based Approach to remove Baseline drift in Biomedical Signals

93

REFERENCES

2

0

A)

1.

-2

-4 2

2.

0

B) -2

-4 2

3.

0

C) -2

4.

-4 2

5.

0

D) -2

-4 2

6.

0

E) Muestras

-2

-4

0

500

1000

1500

2000

2500

7.

Samples

Fig. 2

A. Input Signal (Real ECG). B. FIR Filter C. LMS Filter D. Wavelet Filter E. Proposed Filtrate Systems

8. 9.

V. CONCLUSIONS This paper introduces an approach for removing baseline in ECG signals using neural networks. The approach can easily be combined with other wavelet-based ECG preprocessing techniques such as noise reduction and power line interference reduction. The paper illustrates the effectiveness of the approach by using examples with both simulated and measured ECG data. The current algorithm uses simultaneous perturbation which shows better results for noise reduction. The target application of our algorithm is pre-processing of ECG signals. The signal cancellation depends on the convergence of the method with the LMS methods. The results obtained are quite good with systems FIR and Wavelets (biorthogonal 6.8). However, the System based on Neural Networks, is the better to eliminate the baseline noise. It is possible to emphasize as well, that this last method, it is effective in high processing speed, it is easier to implement, it provokes low signal distortion and it needs minimum memory requirement.

ACKNOWLEDGMENT This work was partly funded by the project PAC-05-0081 from Consejería de Educación de la Junta de Comunidades de Castilla-La Mancha, GV06/299 from Consellería de Empresa, Universidad y Ciencia de la Generalitat Valenciana and TEC2007--64884 from the Spanish Ministry of Science and Education.

10. 11. 12. 13. 14. 15. 16. 17. 18.

Sörnmo L, Laguna P. (2005) Bioelectrical Signal Processing in Cardiac an Neuro-logical Applications. Elsevier Academic Press. Zheng Xiaoyun Wang Zhigang WN, Lan X. (1997) Restraining respiratory baseline drift of impedance cardiogram signals using wavelet transform. Journal of Chongquing University Natural science edition 1997; 20(5):58.62. Lian Y, HO P. (2004) ECG noise reduction using multiplier-free FIR digital filters. Proceedings of 2004 International Conference on Signal Pro- cessing 2004;2198.2201. Sörnmo L.. (1991) Time-varying filtering for removal of baseline wander in exercise ECGs. Computers in Cardiology 1991; 145.148. Laguna P.,J ane R. and Caminal P.(!992) Adaptive filtering of ECG baseline wander. Engineering in Medicine and Biology Society Proceedings of de Annual International Conference of the IEEE 1992;508.509. Chiu CC, Yeh SJ. (1997) A tentative approach based on wiener filter for the reduction of respiratory effect in pulse signals. Proc.19th Int Conf IEEEEMBS 1997;1394.1397. Lisheng Xu DZ, Wang K. (2005) Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms. IEEE Trans Biomed Eng 2005;53(11):1973.1975. Nibhanupudi S. Signal Denoising Using Wavelets. Ph.D. thesis, University of Cincinnati, 2003. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000 (June 13);101(23):e215.e220. Haykin S., (1994) Neural Networks: A Comprehensive Approach, IEEE Computer Society Press 1994, Piscataway, USA Lehtokangas M.,(1999) Fast Initialization for Cascade-Correlation Lear- ning, IEEE Trans. on Neural Networks , 10 (2), 410-414. Sanchez G. K. Toscazo, M. Nakano and H. Perez. (2001) A growing cell neural network structure with back propagation learning algorithm. Telecommunications and Radio Engineering ;56(1):37-45 Hodge V.:(2001) Hierarchical Growing Cell Structures, Trees GCS, IEEE Trans. on Knowledge and Engineering , 13 (2), 207-218. Schetinin V.(2003) A Learning Algorithm for Evolving Cascade Neural Networks, Neural Letters , 17 (1), 21-3. Maeda Y. and R.J.P. De Figueiredo, (1997) Learning Rules for Neuro-controller via Simultaneous Perturbation, IEEE Trans. on Neural Networks ; 8 (6), 1119-1130. Spall J.C., A Stochastic Approximation Technique for Generating Maximum Likelihood Parameter Estimates, Proc. of The American Control Conference 1987, 1161-1167 Spall J.C., y J.A. Criston, Nonlinear Adaptive Control Using Neural Networks: Estimation with a Smoothed Form of Simultaneous Perturbation Gradient Approximation, Statistical Sinica 1994, 4, 1-27, Kathivalavakumar I. y P. Thangavel; (2003) A New Learning Algorithm Using Simultaneous Perturbation with Weight Initialization, Neural Letters , 17 (1), 55-68. Author: Jorge Mateo Sotos Institute: City: Country: Email:

Innovation in Bioengineering Research Group Cuenca Spain [email protected]

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Non-Linear Organization Analysis of the Dominant Atrial Frequency to Predict Spontaneous Termination of Atrial Fibrillation R. Alcaraz1 and J. J. Rieta2 1

Innovation in Bioengeeniering Research Group, University of Castilla-La Mancha, Cuenca, Spain 2 Biomedical Synergy, Valencia University of Technology, Valencia, Spain

Abstract— The prediction of spontaneous atrial fibrillation (AF) termination or maintenance could avoid unnecessary therapy and contribute to take the appropriate decisions in the management of the arrhythmia. The aim of this work is to predict if an AF episode terminates spontaneously or not. The prediction was carried out making use of non-linear organization analysis applied to the surface ECG. Sample entropy was selected as organization index, proving that atrial activity (AA) organization increases prior to AF termination. Using the dominant atrial frequency organization analysis, that is the frequency-selected signal produced by the main reentry wandering the atrial tissue, 92% of the terminating and nonterminating analyzed AF episodes were correctly classified. Because noise and ventricular residues degrade AA organization estimation performance, the use of selective filtering to get the dominant atrial frequency was necessary. The obtained outcomes allow to conclude that the dominant atrial frequency, and therefore, the main atrial reentry, contains the most relevant information about spontaneous AF termination. Keywords— Atrial Fibrillation, ECG, Atrial Activity, Sample Entropy, Organization.

I. INTRODUCTION Atrial fibrillation (AF) is the most commonly diagnosed sustained arrhythmia in the clinical practice and affects up to 1% of the general population. Considering its prevalence with age, this arrhythmia affects up to 15% of the population older than 80 and has an incidence that doubles with each advancing decade [1]. There exist evidence that AF is one of the main causes of embolic events that, in 75% of the cases, develop complications associated with cerebrovascular accidents, provoking that a patient with AF has twice the risk of death than a healthy person [2]. AF episodes that terminate spontaneously (paroxysmal AF episodes) are the preceding stage of permanent AF episodes, which only terminate applying pharmacological, electrical or surgical intervention. Permanent AF patients have a high embolic accidents risk and about 18% of paroxysmal AF degenerate into permanent AF in less than 4 years [1]. Therefore, the early prediction of AF maintenance is crucial, because appropriate interventions may terminate the arrhythmia and prevent AF chronification. In contrast,

the spontaneous AF termination prediction could avoid unnecessary therapy, and therefore, reduce the associated clinical costs and improve the patient’s quality of life. The aim of this work is to predict if an AF episode terminates spontaneously making use of surface electrocardiogram (ECG) recordings, which can be easily obtained. The decrease in the number of reentries prior to AF termination produces simpler wavefronts into the atrial tissue [3], and f waves, that characterize the ECG of an AF patient, evolve to P waves, which characterize a normal ECG with sinus rhythm [2]. Therefore, the atrial activity (AA) becomes more organized before AF termination [4] and, as a consequence, this fact can be used to predict AF termination when the proper analysis tools are used. Previous groups studied non-linear complexity indexes to characterize the atrial activity degree of organization, but obtained results did not reveal significative differences between terminating and non-terminating AF episodes. The low signal to noise ratio was believed to be the main reason to this unsuccessful result [5]. Thereby, in the present work, an selective filtering filter process adapted to the peak atrial frequency was used to reduce noise, ventricular residues and other nuisance signals. The obtained signal organization was estimated by means of sample entropy being able to discriminate between terminating and non-terminating AF episodes. This non-linear tool quantifies the regularity of a time series [6] and its use in this study is justified because (i) the non-linearity, as necessary condition for a chaotic behavior, is present in the heart with AF at cellular level and (ii) the electrical remodelling in AF is a far-fromlinear process [7]. II. MATERIALS A. Database The used database contained 50 one minute and two leads (II and V1) electrocardiogram (ECG) recordings, which were available in Physionet [8]. They were extracted from 24-hour Holter recordings from 50 different patients. The database included non-terminating AF episodes (group N), which were observed to continue in AF for at least one

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Non-Linear Organization Analysis of the Dominant Atrial Frequency to Predict Spontaneous Termination of Atrial Fibrillation

hour following the end of the excerpt, and AF episodes terminating immediately after the end of the extracted segment (group T). Recordings have been divided into a learning and a test set. Next, 10 labelled recordings of each group formed the learning set. An optimal threshold, which should allow to discern between terminating and non-terminating paroxysmal AF episodes, was defined making use of the proposed methodology together with the learning set. Finally, the test set was composed with the remaining 30 recordings. B. Data Preprocessing The ECG recordings were preprocessed in order to reduce noise, nuisance interferences and improve later analysis. Firstly, baseline wander was removed making use of bidirectional high pass filtering with 0.5 Hz cutt-off frequency [9]. Secondly, high frequency noise has been reduced with a eight order bidirectional IIR Chebyshev low pass filtering, whose cut-off frequency was 70 Hz [10]. Finally, powerline interference has been removed through adaptive filtering, which preserves the ECG spectral information [11]. III. METHODS

Sample Entropy (SampEn) examines time series for similar epochs and assigns a non-negative number to the sequence, with larger values corresponding to more complexity or irregularity in the data [12]. Two input parameters, a run length m and a tolerance window r, must be specified for SampEn to be computed. SampEn(m,r,N) is the negative logarithm of the conditional probability that two sequences similar during m points remain similar at the next point, where self-matches are not included in calculating the probability. Thus, a lower value of SampEn also indicates more self-similarity in the time series. Formally, given N data points from a time series {x(n)}=x(1),x(2),...,x(N), SampEn can be defined as follows: 1. Form m vectors defined by Xm(1),...,Xm(N-m+1), for 1≤i≤N-m+1. These vectors represent m consecutive x values, starting at the ith point. 2. Define the distance between vectors Xm(i) and Xm(j), d[Xm(i),Xm(j)], as the absolute maximum difference between their scalar components: max (|x(i + k ) − x( j + k )|) d [X m (i ), X m ( j )] = k=0,... ,m −1

3. For a given Xm(i), count the number of j (1≤j≤N-m, j≠i), denoted as Bi, such that the distance between Xm(i) and Xm(j) is less than or equal to r. Then, for 1≤i≤N-m, 1 Bi N − m −1

(2)

1 ∑ Bim (r ) N −m

(3)

Bim (r ) =

4. Define Bm(r) as Bm (r ) =

5. Increase the dimension to m+1 and calculate Aias the number of Xm+1(i) within r of Xm+1(j), where j ranges from 1 to N-m (j≠i). We then define Aim(r) as Aim (r ) =

1 Ai N − m −1

(1)

(4)

6. Set Am(r) as A m (r ) =

1 ∑ Aim (r ) N −m

(5)

Thus, Bm(r) is the probability that two sequences will match for m points, whereas Am(r) is the probability that two sequences will match for m+1 points. Finally, sample entropy can be defined as ⎧ A m (r )⎫ SampEn(m, r ) = lim ⎨− ln m ⎬ B (r )⎭ N →∞ ⎩

A. Sample Entropy

95

(6)

which is estimated by the statistic ⎡ A m (r ) ⎤ SampEn(m, r, N ) = −ln ⎢ m ⎥ ⎣ B (r ) ⎦

(7)

Although m and r are critical in determining the outcome of SampEn, no guidelines exist for optimizing their values. The m and r values suggested by Pincus are m=1 or m=2 and r between 0.1 and 0.25 times the standard deviation of the original time serie {x(n)} [7]. B. Main Atrial Wave Derivation and Classification The proposed methodology to obtain the MAW is shown in Fig. 1. Firstly, cancellation of QRST waves from the ECG signals has been performed, obtaining a raw approximation of the MAW. Though a variety of QRST cancellation techniques exist, the average QRST template cancellation method has been used, since only two leads were available [13]. Next, the power spectral density (PSD) of the residual signal was calculated using Welch Periodogram. A Hamming window of 4096 points in length, a 50% overlapping between adjacent windowed sections and a 8192points Fast Fourier Transform (FFT) were used as computa-

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R. Alcaraz and J. J. Rieta

tional parameters. The frequency with the largest amplitude within the 3-9 Hz range was selected as the dominant atrial frequency. Finally, the MAW was obtained applying a selective filtering to the AA signal centered around the dominant atrial frequency. To prevent distortion, a linear phase FIR filter has been used [14]. Chebyshev approximation was preferred because all the filter parameters can be suitably fitted and minimum ripple in the pass and stop bands were needed. Therefore, a high order filter should be used, such as the Kaiser approximation marks [15]: M=

− 20log 10

(

)

∂ 1 ∂ 2 − 13

14.6 Δf

+1

(8)

where M is the filter order, ∂1 and ∂2 are the pass and stop bands ripple, respectively, and Δf is the transition bandwidth between bands. A selective filter must have and lower than 0.5% of the gain and Δf lower than 0.01 Hz, thereby its order must be greater than 250. The selected filter bandwidth should be lower than 6 Hz because the typical frequency range of the AA is around 39 Hz [16]. In our experiments, the best results were obtained with a 3 Hz bandwidth and 768 filter coefficients. The MAW organization results obtained through the application of SampEn to the learning set, defined the optimum threshold (Th) that, later, will allow the test set classification into terminating and non-terminating AF episodes. IV. RESULTS The proposed methodology was applied to the learning set and 100% sensivity and 90% specificity were obtained, see Fig. 2(a). The Receiver Operating Characteristic (ROC) curve provided 0.089115 as optimum SampEn discrimination threshold between terminating and non-terminating AF sets. Fig. 2(b) shows the SampEn values for the 20 learning signals together with the mean and standard deviation values for each group. Note that all non-terminating and 9 out of 10 terminating recordings (95% of the learning signals) can be correctly discriminated. Making use of the aforementioned SamEn threshold, 27 out of 30 test signals (90%) were correctly classified, see Fig. 2(c). Therefore, the AF behaviour of 46 out of 50 recordings (92%) were correctly predicted through the organization analysis of the main atrial wave. The terminating episodes present lower SampEn values than the non-terminating ones, see Table 1. Indeed, both paroxysmal AF groups are statistically distinguishable, given that statistic significance is notably lower than 0.01.

Table 1. Mean value and standard deviation for T and N sets and the tstudent statistical significance.

mean ± std p

N Group

T Group

0.1047 ± 0.01352

0.0747 ± 0.0156

0.00000000245

Finally, remark that the obtained SampEn values are quite low, because of the MAW is a notably regular wave. V. DISCUSSION AND CONCLUSIONS By analyzing with sample entropy main atrial wave organization prior to spontaneous AF termination, 92% of the terminating and non-terminating AF episodes were correctly classified. Obtention of the MAW was necessary because direct SampEn analysis of the atrial activity did not revealed significative differences between terminating and nonterminating AF recordings. The presence of noise and ventricular residues is believed to be the main reason to this negative result [5]. In previous works different characteristics of the atrial activity have been analyzed in order to predict AF termination. Petrutiu et al. [17] studied the AA dominant frequency, whose inverse is the wavelength of the main reentry into the atrial tissue [18], and 94% of the episodes were correctly classified. Nilsson et al. [5] studied the AA energy distribution predicting successfully 88% of the recordings. The results obtained with the presented methodoloy allow to conclude that the MAW, and therefore, the main reentry, contains important information about spontaneous AF termination. Moreover, the AF terminates when the MAW disappears [19], because of the mechanisms that extinguish the MAW affect the existing reentries. So, an in deep analysis of the MAW should improve understanding of AF termination mechanisms. Terminating episodes present lower SampEn values and, consequently, higher organization than non-terminating episodes. This observation corroborates the organization increment in the atrial activity prior to AF termination obtained with invasive atrial electrograms [20] and clinically accepted [4]. In addition, the obtained results prove that this increase in the signal organization is reflected on surface ECG recordings. Therefore, it can be concluded that clinical relevant information can be extracted through sample entropy-based non invasive analysis. This fact, may lead towards the development of improved therapeutic interventions for the treatment of AF.

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Non-Linear Organization Analysis of the Dominant Atrial Frequency to Predict Spontaneous Termination of Atrial Fibrillation

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Fig. 1. Block diagram describing the proposed methodology. Firstly, the ventricular activity is removed from the input ECG to obtain the AA. Next, a low bandwidth selective filter is applied to the atrial signal. The filter is centered around the dominant atrial frequency. SampEn is computed on the resulting signal and threshold compared in order to decide if the episode terminates.

Fig. 2.

Obtained results. (a) Receiver Operating Characteristic (ROC) Curve obtained with the learning set estimated SampEn values. Classification into non-terminating and terminating AF for the recordings in (b) learning set and (c) test set.

VI. ACKNOWLEDGMENT This work was partly supported by the project GV06/299 from Consellería de Empresa, Universidad y Ciencia de la Generalitat Valenciana and TEC2007–64884 from the Spanish Ministry of Science and Education

REFERENCES V. Fuster, L. E. Ryden, R. W. Asinger, and et al., “ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the European Society of Cardiology committee for practice guidelines developed in collaboration with th european heart rhythm association and the heart rhythm society,” Europace, vol. 8, no. 9, pp. 651–745, 2006. 2. C. Blomstrom-Lundqvist, M. M. Scheinman, E. M. Aliot, J. S. Alpert, and et. al., “ACC/AHA/ESC guidelines for the management of patients with supraventricular arrhythmias,” European Heart Journal, vol. 24, no. 20, pp. 1857–1897, 2003. 3. K. Konings, C. Kirchhof, J. Smeets, H. Wellens, O. Penn, and M. Allessie, “High-density mapping of electrically induced atrial fibrillation in humans,” Circulation, vol. 89, pp. 1665– 1680, 1994.

1.

4. A. Bollmann and F. Lombardi, “Electrocardiology of atrial fibrillation - Current knowledge and future challenges,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 6, pp. 15–23, 2006. 5. F. Nilsson, M. Stridh, A. Bollmann, and L. Sornmo, “Predicting spontaneous termination of atrial fibrillation using the surface ECG,” Medical Engineering & Physics, vol. 8, pp. 802– 808, 2006. 6. S. M. Pincus, “Approximate entropy as a measure of system complexity,” in Proc. Natl. Acad. Sci. USA, vol. 88, no. 6, pp. 2297–2301, 1991. 7. A. Bollmann, “Quantification of electrical remodeling in human atrial fibrillation,” Cardiovasc Res, vol. 47, pp. 207–209, 2000. 8. A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, and et al., “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215– e220, 2000. 9. I. Dotsinsky and T. Stoyanov, “Optimization of bi-directional digital filtering for drift suppression in electrocardiogram signals,” J. Med. Eng. Technol., vol. 28, no. 4, pp. 178–180, 2004. 10. Y. Sun, K. Chan, and S. M. Krishnan, “ECG signal conditioning by morphological filtering,” Comput Biol Med, vol. 32, no. 6, pp. 465–479, 2002.

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98 11. M. Ferdjallah and R. E. Barr, “Adaptive digital notch filter design on the unit-circle for the removal of powerline noise from biomedical signals,” IEEE Transaction on Biomedical Engineering, vol. 42, no. 6, pp. 529–536, 1994. 12. J. S. Richman and J. R. Moorman, “Physiological time series analysis using approximate entropy and sample entropy,” Am J Physiol, vol. 278, no. 6, pp. H2039–H2049, 2000. 13. J. Slocum, A. Sahakian, and S. Swiryn, “Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity,” Journal of Electrocardiology, vol. 25, no. 1, pp. 1–8, 1992. 14. L. Sörnmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications.1em plus 0.5em minus 0.4emElsevier Academic Press, 2005. 15. L. R. Rabiner, J. H. McClellan, and T. W. Parks, “FIR digital filter design techniques using weighted chebyshev approximation,” Proc. IEEE, vol. 63, pp. 595–610, 1975. 16. M. Stridh and L. Sornmo, “Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation,” IEEE Trans. Biomed. Eng, vol. 48, no. 1, pp. 105–111, 2001. 17. S. Petrutiu, A. V. Sahakian, J.Ng, and S. Swiryn, “Analysis of the surface electrocardiogram to predict termination of atrial

R. Alcaraz and J. J. Rieta fibrillation: the 2004 computers in cardiology/physionet challenge,” Computers in Cardiology, pp. 105 – 108, 2004. 18. M. Holm, S. Pehrson, M. Ingemansson, L. Sörnmo, R. Jahansson, L. Sandhall, and et. al, “Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method,” Cardiovasc. Res., vol. 38, no. 1, pp. 69–81, 1998. 19. J. Kneller, J. Kalifa, R. Q. Zou, A. V. Zaitsev, M. Warren, O. Berenfeld, and et al, “Mechanisms of atrial fibrillation termination by pure sodium channel blockade in an ionicallyrealistic mathematical model,” Circulation Research, vol. 96, no. 5, pp. E35–E47, 2005. 20. L. Faes, G. Nollo, R. Antolini, F. Gaita, and F. Ravelli, “A method for quantifying atrial fibrillation organization based on wave-morphology similarity,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 12, pp. 1504–1513, 2002. Author: Institute: Street: City: Country: Email:

Raúl Alcaraz Martínez Innovation in Bioengeeniering Research Group E. U. Politécnica, Campus Universitario Cuenca Spain [email protected]

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Phase-Rectified Signal Averaging for the Detection of Quasi-Periodicities in Electrocardiogram R. Schneider1, A. Bauer1, J.W. Kantelhardt2, P. Barthel1 and G. Schmidt1 1

1. Medizinische Klinik und Deutsches Herzzentrum München, Technische Universität München, Munich, Germany 2 Institute of Physics, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany

Abstract— The methods analyzing heart rate variability (HRV) have to deal with non-stationary and noisy signals. We present a recently developed method, called phase-rectified signal averaging (PRSA), which facilitates the processing of such signals. It provides a higher sensitivity for the detection of small oscillations in the heartbeat tachogram and it allows the separate analyzes of oscillations related to heart rate decelerations and heart rate accelerations. Because of these properties, PRSA enables a better risk stratification in post myocardial infarction patients than the standard HRV parameters. Keywords— Heart rate variability, autonomic nervous system, phase-rectified signal averaging.

I. INTRODUCTION Analysis of heart rate variability (HRV) is a non-invasive technique which provides information about the function of the autonomic nervous system. Several studies have shown that assessing the status of the autonomic nervous system is a useful method for assessing the mortality risk of cardiac patients [1-4]. Heartbeat intervals are modulated by the autonomic nervous system with high brain centers responding to varying inputs from the heart, the lung and blood vessels. Therefore, periodicities in the heart rate tachogram occur on different time scales and are only stationary over a short period of time. Standard HRV parameters are usually calculated using 24-hour ECG recordings and are either based on the heartbeat intervals directly (time domain) or from the power spectrum of the heartbeat intervals (frequency domain) [2]. When calculating the power spectrum with standard Fourier analysis, the non-stationarities in the heartbeat intervals (changes in the amplitude, frequency and phase) results in a spectrum showing predominantly 1/f noise. Only distinct oscillations caused by the autonomic nervous system are visible, weak and transient oscillations are masked by the noise. In this presentation we will describe a recently developed method, termed phase-rectified signal averaging (PRSA) [58], which allows the extraction of periodic elements from non-stationary and noisy signals.

II. PHASE-RECTIFIED SIGNAL AVERAGING The basic principle of PRSA is the alignment of segments of a signal relative to selected anchor points followed by averaging these segments. The complete PRSA procedure can be divided into five steps: (1) defining anchor points, (2) defining segments around each anchor point, (3) phase rectification, (4) signal averaging and (5) quantification of the transformed signal. Figure 1 show these five steps applied on a 24-hour ECG. The next sub-sections will explain each step. A. Definition of Anchor Points At the beginning, anchor points are selected according to certain properties of the signal. A very simple criterion is an increase (or alternatively decrease) of a heartbeat interval. This means, that each interval which is longer (or shorter) than the preceding RR interval is selected as an anchor point. In the graph showing the heartbeat intervals in figure 1 step A, the selected anchor points related to an interval prolongation are marked as full circles (the anchor points related to an interval shortening are marked as open circles). B. Definition of Segments In the next step, segments around each single anchor point are selected. All segments have the same size and the segments can overlap. The length of the segments is defined by the lowest frequency to be visualized. C. Phase rectification The defined segments are now aligned in this way that the anchor points in the segments are on top of each other. Figure 1C shows the alignment schematically; in figure 1D the aligned signal segments are the light grey lines.

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Phase-Rectified Signal Averaging for the Detection of Quasi-Periodicities in Electrocardiogram

heartbeat interval

ECG

mV

39

A.Definition of anchors Heartbeat interval (ms)

ν2

νn

ν3

ν1

B.Definition of segments

C.Phase-rectification S1 S2 S3

νi

L

Sn

D. Signal averaging

E. Quantification

Heartbeat interval (ms)

DC(AC)=[X0+X1-X-1-X-2]/4 1100

X0

846

X1

1000 900

838

800

X -2

-60

X -1

830

700 -40

- 20

0

i

20

40

60

-8

-4

0

4

8

i

Fig. 1 Phase-rectified signal averaging applied to a 24-hour ECG. (A) Definition of anchor points: heartbeat intervals which are longer (shorter) than the previous interval are marked with full circles (open circles). (B) Definition of segments: around each anchor point (here interval prolongations are used) segments with the same size are selected. (C) Phase rectification: Segments are aligned at the anchors. (D) Signal averaging: Averaging of the selected segments to get the PRSA signal. (E) Quantification: measuring the central part of the PRSA signal using a Haar wavelet with s=2 and p=0. (From [6] with permission.)

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R. Schneider, A. Bauer, J.W. Kantelhardt, P. Barthel and G. Schmidt

D. Signal Averaging A 120

III. PROPERTIES OF PRSA Figure 2 shows – for a 24hour ECG - the original tachogram (A), the power spectrum of the tachogram (B), the PRSA transformed signal (C) and the power spectrum of the PRSA signal (D). As can be seen, both power spectra show two peaks, at the same frequency but in the PRSA signal based spectrum, the frequency components are more distinct than in the tachogram based spectrum. The PRSA transformation improves the signal-to-noise ratio while preserving the frequency content of the original signal. Furthermore, the length of the PRSA signal is much shorter than the tachogram (~80.000 vs. 100). The PRSA signal can be seen as a compressed version of the tachogram containing all the relevant frequencies. What is lost is the temporal association of the oscillations. A detailed theoretical description of the PRSA method can be found in [5] and [7].

106

B

104 102

80

100 40 0

E. Quantification

2

4

6 8 *10 # RR Interval

10-2 -2 10

4

C 110

-1 10 frequency f [interval -1]

PRSA Spectrum Power (ms2)

Phase-Rectified Signal Average RR interval (ms)

For the quantification of the PRSA signal, we suggest to use wavelet analysis. This allows selecting both the time scale s and the time position p relative to the center (the anchor points). For analyzing heartbeat intervals we are using a Haar wavelet with s = 2 and p = 0. With these settings, the central part of the PRSA signal (representing the contribution of all oscillations) is analyzed. The result of the wavelet analysis is called deceleration capacity (DC) or acceleration capacity (AC) when using heartbeat interval prolongations or heartbeat interval shortenings as the selection of the anchor points, respectively. Figure 1E shows the central part of the PRSA signal and how the calculation of DC and AC is performed (the only difference in the calculation of DC and AC is in step A when defining the anchor points using different selection criteria).

Power (ms2)

RR interval (ms)

To get the PRSA signal X(i), the signal values within the aligned segments are averaged – i.e. X(0) is the average of the RR intervals at all anchor points, X(1) and X(-1) are the averages of the RR intervals immediately following and preceding the anchor points, etc. The black line in figure 1D is the resulting PRSA signal.

Power Spectrum

Tachogram

10 3 10

D

2

10 1

109

10 0 108

-40

-20

0

20 40 # RR Interval

10 -1 -2 10

-1 10 frequency f [interval -1]

Fig. 2 Comparison of (A) a tachogram, (B) the power spectrum of the tachogram, (C) the PRSA transformed signal of the tachogram using RR interval prolongations as anchor points and (D) the power spectrum of the PRSA signal. In both power spectra two frequency peaks can be identified (marked with arrows) but the peaks in the PRSA spectrum are more prominent; the signal-to-noise ratio is much better. (From [5] with permission.) IV. CLINICAL APPLICATION Figure 3 shows representative PRSA signals of three post myocardial infarction patients. For each patient, the PRSA signals related to heart rate decelerations and accelerations are shown on the left and right side respectively. The top panel (figure 3 A and B) shows normal PRSA signals, the patient from whom the ECG was taken survived the follow-up period. Both PRSA signals are symmetric and the magnitude of DC and AC are nearly identical. The middle panel (figure 3 C and D) shows the data from a patient who died three month after the index infarction. The PRSA signals are blunted and symmetric and the magnitudes of both PRSA parameters are significantly smaller. Finally, in the bottom panel (figure 3 E and F) are the PRSA signals from a patient who died 5 month after the index infarction. In this case, the PRSA signals are asymmetric and the magnitudes of DC and AC are different. The acceleration related parameter AC has a normal value but the deceleration related value DC has a pathologic value. We found this asymmetric pattern in 15% of our patients [6].

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Phase-Rectified Signal Averaging for the Detection of Quasi-Periodicities in Electrocardiogram

When applying PRSA to 24-hour ECGs from myocardial infarction patients, the DC parameter is highly associated with mortality whereas AC has only a weak association with mortality. DC can differentiate patients with high risk (DC ≤ 2.5ms), intermediate risk (DC between 2.6 ms and ≤ 4.5 ms) and low risk (DC > 4.5 ms) [6]. Acceleration related PRSA signal

Deceleration related PRSA signal Heart Beat Interval (ms)

Survivor

850

A

DC= 5.1ms

B

AC= -5.2ms

V. CONCLUSION The phase-rectified signal averaging method facilitates the detection of oscillations in biological signals; it provides a much better signal-to-noise ratio than the standard Fourier transformation [5,7]. Furthermore, it allows the separate investigation of heart rate deceleration related HRV and acceleration related HRV. As shown in [6], DC is a better risk predictor of mortality after myocardial infarction than not only the standard HRV parameters but also the left ventricular ejection fraction.

845

REFERENCES

840

1.

835

Non-survivor

Heart Beat Interval (ms)

2. 700 C

DC= 2.4ms

D

AC= -2.5ms

695

3.

690

4. 5.

685

Non-survivor

865 Heart Beat Interval (ms)

41

E

DC= 2.5ms

F

AC= -5.0ms

6.

860

7. 855

8.

850 -60 -40 -20

0

i

20

40

59 -60 -40 -20

0

i

20

40 59

Fig. 3 Representative PRSA signals of 24-hour ECGs from post myocardial infarction patients. (A) and (B) are from a patient who survived the follow-up period, both DC and AC are normal. (C) and (D) are from a patient who died 3 month after the index infarction, both DC and AC are abnormal. (E) and (F) are from a patient who died 5 month after the index infarction, the PRSA signal-pattern are asymmetric, DC is abnormal but AC is normal. (From [6] with permission.)

Kleiger RE, Miller JP, Bigger JT et al. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. American Journal of Cardiology 1987; 59: 256-62. Task Force of the European Society of Cardiology and the American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043-65. Schmidt G, Malik M, Barthel P et al. Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 1999; 353: 1390-6. Barthel P, Schneider R, Bauer A et al. Risk Stratification After Acute Myocardial Infarction by Heart Rate Turbulence. Circulation 2003; 108: 1221-6. Bauer A, Kantelhardt JW, Bunde A et al. Phase-rectified signal averaging detects quasi-periodicities in non-stationary data. Physica A 2006; 364: 423-34. Bauer A, Kantelhardt JW, Barthel P et al. Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. Lancet 2006; 367:1674-81. Kantelhardt JW, Bauer A, Schumann AY et al. Phase-rectified signal averaging for the detection of quasi-periodicities and the prediction of cardiovascular risk. Chaos (in press). Bauer A, Deisenhofer I, Schneider R et al. Effects of circumferential or segmental pulmonary vein ablation for paroxysmal atrial fibrillation on cardiac autonomic function. Heart Rhythm 2006; 3: 1428-35. Address of the corresponding author: Author: Institute: Street: City: Country: Email:

Raphael Schneider Munich University of Technology, 1st Medical Clinic Ismaninger Str. 22 81675 Munich Germany [email protected]

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QT Intervals Are Prolonging Simultaneously with Increasing Heart Rate during Dynamical Experiment in Healthy Horses P. Kozelek, J. Holcik Czech Technical University in Prague, Faculty of Biomedical Engineering, nam. Sitna 3105, 27201 Kladno E-mail:[email protected], Phone: +420 312 608 213, Fax: +420 312 608 204 Abstract – Prolonging of QT intervals was published numerously and it is considered as a significant factor in prediction of sudden death. Available sources are occupied by analysis of static re-cords, i.e. without transient processes. We have recorded ECG data in horses that show clear prolonging of QT intervals during increasing heart rate in dynamical experiment. Generally speaking it is suggested as great risk of sudden heart failure. Paradoxically those records were measured in well trained horses. We suppose that such a phenomenon is caused by changes in nervous control of a heart. We have developed several model structures of QT intervals control. Simulation results indicate that generally accepted attitude to QT intervals control is misguiding or incomplete. We have proved theoretically that the abnormalities are caused by variant sensitivity of ventricular cells to sympathetic and parasympathetic during repolarisation phase. I. INTRODUCTION

Past studies on controlling the myocardium based on the cardiovascular system anatomy show that there are two mechanisms (stimulating and inhibiting) controlling performance of heart in organism. The task of our project was to describe mathematically the sympathetic (stimulating) and vagal (inhibiting) activities in a vegetative part of nervous system. Direct measurement of electro-chemical activity of both branches would be very complicated considering practical reasons (invasive measurement; difficulties in connecting the measuring electrodes to the nervous fibres outside of laboratory environment etc.). Therefore, we decided to use data from indirect measurements through the activity of organs, which are controlled by the vegetative nervous system. The anatomy of equine heart shows that open ends of vegetative nerves have a great density close to the sine node which is the basic source of electrical impulses in heart muscle and determines a heart rate. Thus sine node also determines the length of RR intervals in ECG. RR intervals can serve as an indirect indicator of common sympathetic and parasympathetic activity. In our work we assumed mutually independent activities of both branches. Two independent controlling mechanisms are fully described by two signals, therefore, we have to define another nervous activ-

ity indicator. There are open nervous ends of both vegetative branches in equine heart ventricles. That is why a suitable solution for defining another nervous activity indicator was choosing a sequence of QT intervals (time of spreading the electrical excitation through tissue of myocardium ventricles). Based on knowledge of the sequences of RR and QT intervals we designed a model of a myocardium control that could help to explain the causes of control mechanisms of the heart performance [4]. II. METHODS

Block diagram of the model structure is depicted in Fig. 1. As follows from our previous studies [1], [4] we used the formula

RR (t ) = RRSAU − k SR N S (t ) + k PR N P (t )

(1)

for generating sequences of RR intervals. RRSAU is a basic heart period of sine node and NS and NP represent sympathetic and parasympathetic activity levels. kSR, kPR are multiplicative parameters that express levels of influence of each neural branch upon the duration of RR intervals. Similarly,

QT (t ) = QT0 − k SQ N S (t − τ SQ ) + + k PQ N P (t − τ PQ )

(2)

describes an equation generating QT intervals where τSQ and τPQ are delays in sympathetic and parasympathetic neural branches in heart ventricles and kSQ, kPQ are multiplicative parameters, similar to those in the eq. (1). QT0 is a basic length of QT interval at neural ventricular blockade. Both

Fig. 1: Principle structure of the cardiovascular system control

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QT Intervals Are Prolonging Simultaneously with Increasing Heart Rate during Dynamical Experiment in Healthy Horses

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the delays are connected with the finite velocity of nervous stimulation spreading. The aim of extending the described model was to find the internal structure of the subsystems SYM and PSYM (see Fig. 1) and their mutual relationship. The models for simulating static and dynamic properties of both the types of nerves fibres have structures depicted in Fig. 3, as published in [2] and [3]. First of all, it was necessary to describe sensitivity of the fibres on their stimulation represented by input signal NE. In our model, we have used bottom-limited piecewise linear function (Fig. 2) ⎧ ⎪a N + bS , P N S ,P = ⎨ S ,P E ⎪ 0 ⎩

if N E > −

bS , P

aS ,P otherwise

Fig. 2: Sympathetic and/or parasympathetic sensitivities, NS, NP (3)

where indexes “S” and/or “P” represent sympathetic and parasympathetic branch, aS,P is the sensitivity coefficient. Inertia of the nerves is modelled by the first-order low-pass filter described by the frequency response

F ( jω ) =

k T X jω + 1

⋅ e - jωτ X

(4)

where ω represents a frequency, TX is a time constant of the filter, τX is a unit delay and k is a gain of filter. The inertia is associated with the limited delay in response of the cells to their excitation. Finally, the “time-delay” block t represents a final velocity of spreading of the excitation along nervous threads and/or heart tissue. Input signal NE represents response of the neural feedback to impulse stimulation. It is described as

⎧ ⎡ ⎛ 2π π⎞ ⎤ (t − t1 ) − ⎟ + 1⎥, ⎪ A.⎢sin ⎜ 2⎠ ⎦ ⎪ ⎣ ⎝ T if t1 ≤ t ≤ t1 + T NE = ⎨ ⎪ ⎪ 0, otherwise, ⎩

Fig. 3: Basic structure of nervous fibres' model

Fig. 4: Input signal NE

(5)

where T is a duration of the input impulse and t1 represents its lag after some reference starting point. Fig. 5 shows a structure of the nervous heart control. It is based on the hypothesis that QT intervals are not controlled by the nervous activity only, but we can identify an indirect dependency of QT intervals on heart rate and its variability. Fig. 5: Detail structure of nervous control

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P. Kozelek, J. Holcik

objective function

kS

objective function

kS

kP

kP

h

opti ma l path

lp at

op

kS = kP

tim

a

optimum k S = kP

optimum

Fig. 7: Schematic explanation of different behaviour in equine heart

parameters (record 1 – left, record 2 – right)

ventricles (record 1 – left, record 2 – right)

0.1

0.1

0.08

0.08 Objective function

Objective function

Fig. 6: Experimental and simulated data generated by the optimized

0.06 0.04 0.02 Optimum

0 -6

0 -2

kPQ -5

-4

-6

-4 k SQ

0.06 0.04 0.02 Optimum

0 -6

0 -2

kPQ -5

-4

-6

-4 k SQ

Fig. 8: Objective functions for different values of gain factors kSQ, kPQ.

III. RESULTS

The simulation results have been compared to 4 most representative sets of experimental data. The criterion for choosing records was noiseless signal with considerable changes in the sequences of RR and QT intervals as responses to impulse stimulation. The aim of the work is to define properties of controlling subsystems, we have identified some of the model parametres as time constants of the filters (TSRp, TPRp, TSQp, TPQp and/or TSRs, TPRs, TSQs, TPQs), time delays (τSQp, τPQp and/or τSQs, τPQs) and gain factors (kSQp, kPQp and/or kSQs, kPQs). We used Matlab® Optimization Toolbox as an optimization tool. A root mean square error between simulated and real experimental data has been used for an optimization and its minimum was searched by the gradient method. The identification results for two different types of records are summarized in Table 1:

Table 1: Summary of optimized parameters for two different relationships between sequences of RR and QT intervals record 1 (velvet - s el 1 - 2002-04-24) 1. TSR = 25 s

record 2 (nikita - s el - 2002-04-24) 2. TSR = 4 s

3. TPR = 25 s

4. TPR = 4 s

5. TSQ = 19 s

6. TSQ = 18 s

7. TPQ = 17 s

8. TPQ = 18 s

9. τSQ = 18 s

10. τSQ = 4 s

11. τPQ = 20 s

12. τPQ = 1.2 s

13. kSQ = – 5.1

14. kSQ = – 4.2

15. kPQ = – 4.6

16. kPQ = – 4.6

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QT Intervals Are Prolonging Simultaneously with Increasing Heart Rate during Dynamical Experiment in Healthy Horses

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a) direct dependency QT on RR intervals – shortening of the RR intervals is followed by the shortening of QT intervals (Figure 6-left) b) relationship between RR and QT that is not explained yet – shortening of the RR causes almost immediate prolonging of the sequences of QT intervals (Figure 6-right). IV. CONCLUSIONS

Having identified model parameters by means of computer simulations we are able to explain fundamental causes of various behaviour of the QT intervals. We recognised almost linear dependency with positive slope constant between optimum parameters of τSQ, τPQ, and TSQ, TPQ,. It is due to the fact that the developed model uses linear subsystems only (the non-linear functions NS = NS(NE) and NP = NP(NE) are used in their linear parts only) and the input signal NE is used for both sympathetic and parasympathetic branch. We have proved experimentally that the simple shape of the QT interval sequences with one local extreme is generated by the model using very similar values of τSQ, τPQ and TSQ, TPQ in both the neural branches. If the supposition about the similarity of the above mentioned parameters is not valid then the signals NS, NP are mutually shifted in time. The mutual shift will cause a change of the QT sequence shape. In the case, one local extreme is substituted by biphasic waveform with local maximum and minimum (Fig. 9). Dependency of parameters kPQ, kSQ can be approximated well by a linear relationship with a negative slope constant (see the x-y projection of the optimum path in Fig. 7 and Fig. 8) kSQ = a.kPQ + b,

(6)

where the estimated values of coefficients a = −2 and b = −13.5 are roughly valid for all analysed records. If we suppose the simplified criteria τSQ = τPQ and TSQ = TP, then the breaking point between the direct (Fig. 6-left) and inverse (Fig. 6-right) dependency of QT on RR intervals is set for kSQ = kPQ = −4.5. Then for kSQ > kPQ we observe the direct and for kSQ < kPQ the inverse dependency of QT on RR intervals.

Fig. 9: Bi-phase sequences of QT intervals (heda1)

ACKNOWLEDGEMENT The research was granted by the project of Internal Grant Competition in Czech Technical University in Prague.

REFERENCES 1.

2. 3.

Holcik, J., Kozelek, P., Hanak, J. and Sedlinska, M.: Mathematical Modelling as a Tool for Recognition of Causes of Disorders in QT/RR Interval Relationship in Equine ECG. Proc. of PRIA2004, St. Peterburg, Russia, part.III, p.688-691. Van der Voorde, B.J.: Modeling The Baroreflex - a system analysis approach, p.10-59, 136-178. Amsterdam, Netherlands, September 1992. Holcik, J., Kozelek, P., Jirina, M., Hanak, J., Sedlinska, M.: Open-loop Model of Equine Heart Control, 13th NordicBaltic Conference on Biomedical Engineering and Medical Physics, vol. 9, pp. 297–298. ISSN 1680-0737 Author: Institute: Street: City: Country: Email:

Petr Kozelek Czech Technical University in Prague nam. Sitna 3105 272 01 Kladno Czech Republic [email protected]

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Relative contribution of heart regions to the precordial ECGan inverse computational approach A.C. Linnenbank1,2, A. van Oosterom3, T.F. Oostendorp4, P.F.H.M. van Dessel5, A.C. van Rossum2,6, R. Coronel1, H.L. Tan1,5, J.M.T. de Bakker1,2,7 1

Heart Lung Center, Dept. of Exp. Cardiology, AMC, Amsterdam, Netherlands, 2 ICIN, Utrecht, Netherlands 3 Dept. of Cardiology, University of Lausanne, Switzerland, 4 Dept. of Biophysics, Radboud University, Nijmegen, Netherlands, 5 Dept. of Clinical Cardiology, AMC, Amsterdam, Netherlands, 6 Dept. of Cardiology, VUMC, Amsterdam, Netherlands, 7 Dept. of Cardiology, UMC, Utrecht, Netherlands

Abstract— With an inverse computational approach using a multi-lead ECG recording of normal sinus rhythm in a patient spread of activation and repolarization was computed. Using this timing information, contributions to the standard 12-lead ECG were computed for selected segments of the heart. The results show that in none of the precordial leads is the influence of the right ventricle larger than from the left. The electric activity of basal regions and the RVOT are relatively poorly represented in the standard 12-lead ECG. Keywords— body surface mapping, activation time imaging, action potential duration, inverse procedure.

I. INTRODUCTION It is generally assumed that the precordial lead V1 mainly reflects electrical activity from the right ventricle, V2 that of from the interventricular septum and that V3-V6 signals are dominated by the left ventricle. The relative contribution of these cardiac regions to the ECG can not be verified experimentally. Knowledge about the contribution of activation of various parts of the heart to the ECG is important to ascribe ECG abnormalities to certain locations of the heart. Diseases like the Brugada syndrome affect the entire heart but ECG characteristics suggest that abnormalities predominantly arise in the right ventricle (RV). To study the contributions of various parts of the heart to the normal 12-lead ECG we built a (mathematical) volume conduction model of a patient from whom multiple body surface leads were previously recorded. Torso, lungs and heart geometry were reconstructed from magnetic resonance images (MRI). A previously developed inverse method [1,2] was used to estimate the timing of depolarization and repolarization at 1737 nodes specifying the geometry of the heart. The heart was digitally segmented, with triangles added to close the cutting edges. The inversely computed

depolarization and repolarization times were used to specify the local source strength while computing the contributions stemming from these different segments. II. METHODS 65 Channels of ECG data were recorded from a 55 year old female patient. The recordings were made as part of a standard family screening for the presence of the Brugada syndrome. Prior to this test 10 minutes of baseline ECGs were recorded. At baseline the ECG was within normal range and the heart was shown to have no structural abnormalities by MRI.

Fig 1. Left: anterior view of torso, lungs and heart. Right: posterior view. Positions of recording electrodes are indicated by blue dots. Electrodes on other side are visible as gray dots. Lungs are gray. Myocardium is brown. On the right blood volumes connected to the left and right cavities are visible in red and blue respectively

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Relative contribution of heart regions to the precordial ECG-an inverse computational approach

A. MRI and triangulation. A standard cardiac MRI protocol was used to build a 3D numerical model of the heart, lungs and thorax of this patient (Fig. 1), as required for the inverse procedure. From the MRI data slices in the horizontal, transversal and sagital direction, short axis views, and a long axis view were selected. The short axis views were used to create a triangulation of the heart. The same set of slices was used to define the boundaries of the lungs close to the heart to avoid intersection of the organs. The other slices were used to define lung boundaries further away from the heart. Special software to mark points on the slices and convert those to 3D vertices and to combine all these vertices into consistent triangulation was custom written. The thorax was created by deforming an existing mesh (Judy from Poser 5, efrontier.com, Santa Cruz CA) to fit the boundaries in the MRI slices. The electrodes were placed on the thorax at the documented recording sites. B. ECG recordings During a 40 minutes procedure, a 65-lead ECG was continuously recorded at the locations of the Amsterdam lead system [3], at 2 kHz sampling rate, bit_step of 1/8 µV and 24 bits resolution (modified Active2, BioSemi, Amsterdam). Post processing of the signals included the following elements: averaging of 1 minute episodes including rejection of artefacts, reference to zero mean, appropriate baseline correction and selection of the signals elements. For the part of study reported here only the 10 minutes of baseline ECGs were used.

43

electrodes by minimizing the difference between measured and model based potentials. As the relation between activation time and source strength is non-linear, the problem involved constitutes a non-linear parameter estimation problem. Such problems demand the specification of an initial estimate of the solution. Next, the minimization is performed iteratively, for which, in this study, the Levenberg-Marquardt method was used. The quality of the inverse procedure relies heavily on the initial estimate. Several methods to obtain an initial estimate have been described in the literature [2,6]. In the current study we have used a method based on the intra-myocardial distance function as previously described [6,7]. In short, with this method the initial estimate is selected from the exhaustive search of all activation patterns resulting from activations in which, successively, each node is taken as a focus. For each focus the timing of the other nodes was computed from the known distance to the focus and an assumed propagation velocity. The inverse problem is mathematically ill-posed. This is overcome by including the surface Laplacian to constrain the spread of activation to physiologically realistic values [2]. III. RESULTS The inversely computed activation and repolarization times are shown in Figure 2. The earliest activation was

C. Inverse procedure The method used for estimating the timing of the activation at the surface of the ventricular mass was based on the equivalent source model, as used previously [1,2,4]. In short, a tranfer matrix from every source location on the heart to every point on the surface is computed by using the boundary element method. Forward computation of surface potential is performed by multiplying this transfer matrix by simulated action potentials (AP) whose timing and shape depend on activation and repolarization times. A single logistic function was used as a model for the action potential when estimating activation times only. When estimating both activation and repolarization this function was replaced by a model of the action potential that also depends on the repolarization time, specified by a combination of logistic functions [5]. The inverse procedure estimates the activation times at the ventricular surface from the measured potentials at the

Fig 2. Top left: estimated depolarization sequence. Top right: the estimated repolarization times. Below: two views of the heart, showing the difference between the timing of local repolarization and depolarization (APD). The arrows mark an area, which extends into the septum, that has unrealistically short APDs. Times on the color bars below the hearts are in ms

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44 A.C. Linnenbank, A. van Oosterom, T.F. Oostendorp, P.F.H.M. van Dessel, A.C. van Rossum, R. Coronel, H.L. Tan, J.M.T. de Bakker

Fig 3. Contributions to the ECG from the right ventricle (blue), the septum (green) and the left ventricle (red). In black the ECG that was generated by the entire heart. Division of the heart in indicated in the top right

found in the septum, the earliest epicardial activation at the RV free wall close to the septum. Latest activation was at the basal parts of the heart. Figure 2 shows an area in the apex, septum, and posterior apical wall where the repolarization is unrealistic early (arrows). Apart from this area the inversely computed timing is compatible with measured activation patterns [8] . The correspondence between the computed ECG and the measured ECG was excellent (not shown). The RMS difference of measured and simulated potentials was 0.04 mV during the QRS and even less during the T wave. Figure 3 shows the ECGs broken down to its constituent contributions from the LV, RV and septum. Figure 4 shows on the left an enlarged version of V1,V2 and V6 for the same data plus an investigation for the same leads into the contribution of various basal parts of the heart. IV. DISCUSSION The results demonstrate that the electric activity of the LV free wall provides the main contribution to all precordial leads. Even V1 is not dominated by the RV, but receives a contribution from the septum that is just as large, while having an opposite sign. The contribution of the left

Fig 4. Enlarged versions of V1,V2 and V6. On the left the contributing segments studied are: left ventricle (red), septum (green) and right (blue). On the right the RVOT (light blue), the basal part of the right ventricle (blue) and the basal part of the left ventricle (red). Black line indicates contribution of entire heart

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Relative contribution of heart regions to the precordial ECG-an inverse computational approach

ventricular free wall (red line in Fig. 3 and the left part of Fig. 4) matches in this case closely to the recorded V1 lead. In all leads of the 12-leads ECG, the contributions from the basal parts of the ventricles and of the RVOT are very small. The method presented here for studying the contributions to the ECG of selected regions differs in two major respects from looking at contribution maps [9] like the ones available in e.g. ECGSIM [10]. First, we used a realistic activation pattern during sinus rhythm and computed the ECG generated by an entire part of the heart. Second, we used closed surfaces to prevent border artefacts. V. LIMITATIONS A parameterized model of an AP was used that gives a reasonable good shape for action potential durations (APD) over 200ms. For shorter APDs the plateau has a downward slope. This not only influenced the ST and T-wave but may already be noticeable at the end of the QRS. Although the solutions for the repolarization of the baseline ECGs were acceptable when they converged, there were also initial conditions where they diverged into nonphysiological solutions, possibly as a consequence of an interaction of activation and repolarization times. In the solution shown above there is an area at the apex that has much shorter APDs than the surrounding area. This area yields a contribution to the surface ECG that is close to zero. This may be due to the more gradual character of the repolarization, both in time and in space. As a result the inverse estimation of repolarization may have to be regularized over larger areas than the activation. VI. CONCLUSIONS •

•

•

No single lead of the standard 12-lead ECG predominantly represents the electric activity of the RV. Even V1 receives equal or greater contributions from both the septum and the LV. The RVOT, and the basal parts of the LV and RV hardly contribute to the standard leads, at least not during sinus rhythm reported on in this study. To estimate the delay in, e.g., the RV multiple simultaneously recorded signals are required, supported by a dedicated inverse procedure. Estimation of activation (and repolarization) times by an inverse computation and a subsequent forward computation of a part of the heart, can give insight into the way that part contributes to the recorded ECG, information that can not be obtained in any other way.

45

ACKNOWLEDGMENT This study was supported by the Netherlands Heart Foundation grants 2002B087 and 2005B92

REFERENCES Cuppen, J.J.M. and A. van Oosterom, Model studies with the inversely calculated isochrones of ventricular depolarization. IEEE Trans Biomed Eng, 1984. BME-31: p. 652-659. 2. Huiskamp, G. and A. Van Oosterom, The depolarization sequence of the human heart surface computed from measured body surface potentials. IEEE Trans Biomed Eng, 1989. 35(12): p. 1047-1058. 3. SippensGroenewegen, A., et al., A radio-transparent carbon electrode array for body surface mapping during catherization. Proc 9th Ann Conf IEEE-EMBS, 1987: p. 178181. 4. van Oosterom, A. Genesis ot the T wave as based on an Equivalent Surface Source model. JElectrocardiol, 2001. 34 Supl, 217-227. 5. van Oosterom, A. and V. Jacquemet. A Parameterized Description of Transmembrane Potentials used in Forward and Inverse Procedures. in Int Conf Electrocardiol. 2005. Gdansk; Poland: Folia Cardiologica. 6. van Oosterom, A. and P. van Dam. The intra-myocardial distance function as used in the inverse computation of the timing of depolarization and repolarization. in Computers in Cardiology. 2005. 7. Linnenbank et al. Non invasive imaging of activation times during drug induced conduction changes. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Seoul, 2006. 8. Durrer, D et al. Total excitation of the Isolated Human Heart. Circulation. 1970 Jun;41(6):899-912. 9. van Oosterom, A and G.J. Huiskamp. The effect of Torso Inhomogenities on Body Surface Potantials Quantified using “Tailored” Geometry. J. Electrocardiol., 1989, 22, p 53-72. 10. van Oosterom, A. and T.F. Oostendorp. ECGSIM an Interactive tool for Simulating QRST Waveforms. Heart. 2004 Feb;90(2):165-8.

1.

Address of the corresponding author: Author: Institute: Street: City: Country: Email:

A.C. Linnenbank Dept. of Experimental and Clinical Cardiology, AMC Meibergdreef 15 Amsterdam Netherlands [email protected]

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Sample Entropy Analysis of Electrocardiograms to Characterize Recurrent Atrial Fibrillation R. Cervigon1, C. Sanchez1, J.M. Blas1, R. Alcaraz1, J. Mateo1 and J. Millet2 1

Universidad de Castilla-La Mancha. Innovation in Bioengineering Research Group (GIBI). DIEEAC, Cuenca, Spain 2 Universidad Politecnica de Valencia. Bioengineering Electronic Telemedicina, Valencia, Spain

Abstract— In a substantial number of patients atrial fibrillation (AF) recurs after successful electrical cardioversion, but at present there are no reliable clinical markers for confidently identifying the patients in which recurrence will occur within a short period of time. This study evaluates the predictive classification performance of Sample Entropy (SampEn) in the discrimination between recurrent and non-recurrent AF episodes. A validated database of 35 ECG recordings acquired from AF subjects undergoing cardioversion was used throughout the study, together with their known recurrence status at one month. SampEn was applied to these QRST-reduced electrocardiograms, to atrial activity (AA), and also to heart rate (R-R intervals). The sample entropy of R–R intervals was significantly reduced (p=0.043) in the recurrent AF episodes compared with maintenance sinus rhythm episodes. SampEn applied to the AA signal showed a opposite results, it was reduced with a significant increasing trend in the maintenance sinus rhythm episodes (p=0.017). There is a need for welldefined studies with larger patient groups in order to assess the entropy changes further and to look for possible changes, which might predict AF recurrence. Keywords— Atrial Fibrillation, Sample Entropy, Electrical Cardioversion.

I. INTRODUCTION Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in humans. It affects 2% of the unselected adult population and between 6% and 8% of the population over 65 years of age [1,2]. It is the most common cardiac cause of stroke [3]. In addition, the rapid heart resulting from AF can cause a number of adverse outcomes, including congestive heart failure and tachycardia related cardiomyopathy [4], and the risk of mortality is double than in patients with sinus rhythm [5]. AF results from multiple, rapidly changing and spatially disorganized activation wavelets circulate more or less randomly across the atrial myocardium [6,7]. In the surface electrocardiogram, this uncoordinated atrial activity (AA) is reflected as a randomly variable baseline measurement in place of a well-defined P-wave, though in general the subsequent ventricular electrical activity is unaffected in morphology. The progression from normal sinus rhythm (NSR) to AF is not completely understood, though this transition is

often associated with either changes in autonomic tone, or the presence of very early Premature Atrial Contractions (PAC), or atrial tachycardia. Medications are only marginally effective in treating this arrhythmia, and have the potential for serious side effects, including life-threatening pro-arrhythmia. Most of the drugs used to control heart rate in AF do not improve effort tolerance [8], whereas the restoration of sinus rhythm has been shown to significantly improve it [9]. Furthermore, structural cardiac changes associated with AF have been shown to be reversible when sinus rhythm is restored [10]. However, following NSR restoration after successful electrical cardioversion (ECV), the recurrence of AF within a year is up to 60-75% of patients [11]. As a consequence, it is required a reliable predictor for NSR maintenance. A number of clinical studies have been already suggested long term maintenance of sinus rhythm post-cardioversion predictors. Some of them are: left atrial size [12], age, functional class, energy requirements [15], AF duration, and antiarrhythmic drugs [11], though their role for prediction of outcome following cardioversion is controversial. Recent reports have investigated the length of atrial refractory period as an index of effective ECV, but its efficacy is also unclear [12,13,14]. In addition, among factors contributing to genesis or maintenance of circulating wavelets, Autonomous Nervous System (ANS) seems to play a significant pro-arrhythmic role [16]. Table 1 Patients Clinical Characteristics Parameter Patients Men Underlying heart disease Antiarrhytmic Amiodarona Treatment Flecainida

NSR Maintenance 15 (57.14%) 9 (60%) 8 (53%) 12 (80%) 3 (20%)

Redundance AF 20 (43.86%) 16 (80%) 18 (90%) 16 (85%) 4 (15%)

The present study was conducted to analyze ECG signals from patients with persistent AF in order to extract reliable parameters to predict early AF recurrence after successful ECV. The technique employed for ECG analysis was based on the non linear analysis, specifically the entropy, which have been successfully employed to solve other physiologist

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Sample Entropy Analysis of Electrocardiograms to Characterize Recurrent Atrial Fibrillation

problems, as in the analysis of the heart rate variability where has already showed its potential in the prediction of cardiac risk [17,18,19]. II. MATERIALS AND METHODS A. Materials This study was carried out with a signal database including standard 12-lead ECG recordings from 35 patients diagnosed with persistent AF. These recordings were obtained in the Electrophysiological Laboratory, Hospital Clínico Universitario de Valencia, during ECV protocol. These signals consisted in an AF segment before cardioversion. All these signals were digitized at a sampling rate of 1KHz and 16-bit resolution. In order to process these signals, a 1 minute-length AF segment preceding the ECV was extracted for each patient. All patients with AF were monitored 4 weeks after cardioversion, where 15 out of 35 patients (42.86%) remained in NSR, whereas the other 20 patients (57.14%) turned back to AF. B. Preprocessing The analysis was applied to lead V1, which is the lead that shows higher amplitude of the atrial fibrillatory signal. Before applying entropy, all signals were preprocessed using a 50-Hz notch filter to cancel out mains interference, followed by a band-pass filter with cut-off frequencies of 0.5 and 60 Hz to remove baseline wandering and reduce thermal noise. Since the atrial and ventricular activities overlap spectrally, linear filtering techniques are not suitable for extraction of the fibrillatory signal from the surface ECG. Instead, subtraction of averaged QRST complexes needs to be performed producing a remaining atrial fibrillatory signal for further analysis. Although there are different techniques, it was used a fixed averaged QRST-complex for cancellation in individual leads. [14,15] C. Sample Entropy In this study, we have used Sample Entropy (SampEn) as a useful measure of regularity. This is a similar, but less biased, measure than the approximate entropy (ApEn) family of parameters [21] introduced by Pincus to quantify the regularity of finite length time series. The Sample Entropy can be calculated as follows. Consider the distance between two vectors as the maximum of the absolute differences between their components and fix a

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threshold value r for determining when these vectors are close to each other, ApEn reflects the likelihood that sequences that are close to each other, i.e., within r, for m consecutive data points remain close when one more data point is known. Mathematically, ApEn is computed as follows: Let Xi = x1;…; xi ;...; xN represent a time series of length N. Consider the m-length vectors: Um(i)= xi;xi+1;...;xi+m-1. Let nim(r) represent the number of vectors um(j) within r of um(i). SampEn has the advantage of being less dependent on the time series length, showing relative consistency over a broader range of possible r, m, and N values. Starting from the definition of the entropy, it is defined:

SampEn(m, r , N ) = − ln

U m+1 ( r ) U m (r )

The differences between Um+1(r) and Cm+1(r), Um(r) and C (r) are the definition of the distance between two vectors as the maximum absolute difference between their components, the exclusion of self-matches, given a time series with N data points, only the first N-m vectors of length m, um(i), are considered, ensuring that, for 1≤i≤N-m, the vector um+1(i) of length m+1 is also defined. SampEn is precisely equal to the negative of the natural logarithm of the conditional probability that sequences close to each other for m consecutive data points will also be close to each other when one more point is added to each sequence. Larger SampEn values indicate greater independence, less predictability, hence greater complexity in the data. This, in turn, may imply that decreased complexity or greater regularity in the time series is not associated with disease. For the study discussed in this paper, SampEn is estimated using the widely established parameter values of m = 2, and r = 0.25%, where σ represents the standard deviation of the original data sequence, as suggested by Pincus [21]. It assigns higher entropy values to certain pathologic time series than to time series derived from free running physiologic systems under healthy conditions [18,19,20]. m

III. RESULTS The values of SampEn of the R–R intervals (SampEn_RR) were low in the patients with recurrences 1.980±0.012 compare to those that maintenance the sinus rhythm 1.993±0.013 (p < 0.043 Mann-Whitney test) (Fig. 1). The opposite tendency was obtained in the measure of SampEn of the AA (SampEn_AA) with, 1.794±0.135 in the recurrent group vs. 1.554±0.319 in the non recurrent group (p < 0.017, Mann-Whitney test) (Fig. 2).

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points. The area under the curve, measures the ability of the test to correctly classify and assess subjects with and without the disease, such that higher areas indicate higher performance rates. Using this features on a separately training set that quantify the total number of the subjects resulted 71.70 % accuracy with the SampEn_AA and 73.20% from the measure of the SampEn _RR.

2,01

SampEn RR

2,00

1,99

1,98

1,97

Table 2 ROC curve values obtained from SampEn 1,96 0

1

NoRecurrente-Recurrente

Fig. 1 Sample Entropy RR

Threshold

Se (%)

1.755 1.985

73.30 85.50

1-Sp (%) 70.00 62.00

Area 0.738 0.775

To increase the capacity of the prediction, these variables were introduced in a forward stepwise logistic regression model. With SampEn_RR >1.985 (OR 0.080, p=0.008) and SampEn_AA<1.755 (OR 0.206, p=0.007), with a diagnostic capacity of 74.39%, due to the high correlation of the variables, despite of being conceived in different environments, ventricle (SampEn_RR) vs. atrial (SampEn_AA) (Fig. 3).

2,00

1,80

1,60

SampEn AA

Parameter SampEn_AA SampEn _RR

21 1,40

1,20

IV. CONCLUSIONS

1,00

0,80 0

1

NoRecurrente-Recurrente

Fig. 2 Sample Entropy AA The discrimination with the area under the Receiver Operation Characteristic (ROC) curve was performed to assess the correlation between the parameters and AF recurrence. The ROC curve represents sensitivity (SE) versus 1specificity (1-SP), which makes possible the performance evaluation of the detection process at different operation ROC Curve 1,0

Sensitivity

0,8

0,6

0,4

0,2

0,0 0,0

0,2

0,4

0,6

0,8

1,0

1 - Specificity

The role of identifying patients at risk of developing AF recurrence has been studied in numerous Publications, and only variables such as left atrial size and AF duration have been validated and are used in the clinic procedure. The proposed method was applied to evaluate if the non linear qualities of the signals could predict the evolution of the patients after a successful ECV. The results suggest that the ECG signals contain information which provides clues as to the potential recurrence of AF. AA extracted from ECG signals is more regular in patients that maintenance the rhythm, with a low SampEn measures. This difference found coincide with a high organization of the arrhythmia in those episodes where is easier the reversion. On the other hand, the decrease in the complexity of the ventricular rates, such as demonstrates the low values of SampEn measures in the R-R intervals in the recurrent patients, coincides with the results showed by Vikman where a change in the behaviour of the sinus rhythm plays an important paper in the AF recurrences [15]. All these results suggest that non linear study of ECG signals involve much more information than known at present. The results and conclusions presented in this contribution should be regarded as a first that must be extended with further studies, including new experiments with a larger database.

Fig. 3 ROC curve from Regression Logistic model

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Sample Entropy Analysis of Electrocardiograms to Characterize Recurrent Atrial Fibrillation

ACKNOWLEDGMENT The authors would like to acknowledge the helpful support received from Servicio de Hemodinamica of the Hospital Clinico Universitario de Valencia, and specially from R. Ruiz, S Morell and R Garca Civera, for providing signals and for the high quality of their clinical advice.

11. 12. 13.

REFERENCES 1. Levy S, Breithardt G, Campbell RW, Camm AJ, Daubert JC, Allessie M, et al. Atrial fibrillation: current knowledge and recommendations for management. Working Group on Arrhythmias of the European Society of Cardiology. Eur Heart J 1998; 19(9): 1294-320.

2. Benjamin EJ, Levy D, Vaziri SM, D’Agostino RB, Belanger AJ, Wolf PA. Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. JAMA 1994; 271(11): 840-4.

3. Wolf PA, Abbot RD, Kannel WB: Atrial fibrillation as an independent risk factor for stroke: the Framingham Study Stroke 1991; 22: 983-988. 4. Crijns HJ, Tjeerdsma G, de Kam PJ, Boomsma F, Gelder IC, Berg MP, et al. Prognostic value of the presence and development of atrial fibrillation in patients with advanced chronic heart failure. Eur Heart J 2000; 21(15): 1238-45. 5. Wattigney WA, Mensah GA, Croft JB. Increased atrial fibrillation mortality: United States, 1980-1998. Am J Epidemiol 2002; 155(9): 819-26. 6. Moe GK, Abildskov JA. Atrial fibrillation as a self sustaining arrhythmia independent of focal discharge. Am Heart J. 1959; 58: 59–70. 7. Olsson SB, Allessie MA, Campbell RW, eds. In Atrial Fibrillation: mechanisms and therapeutic strategies. Armonk, NY: Futura Pub, 1994: 37–49. 8. Schumacher B, Luderitz B: Rate issues in atrial fibrillation: consequences of tachycardia and therapy for rate control. Am J Cardiol 1998; 82: 29N-36N. 9. Lok NS, Lau CP. Presentation and management of patients admitted with atrial fibrillation: A review of 291 cases in a regional hospital. Int J Cardiol 1995; 48(3): 271-8. 10. Gosselink ATM, Crijns HJGM, Berg MP, Broek SAJ, Hillege H, Landsman MLJ et al. Functional capacity before and after

14.

15.

16. 17.

18.

19. 20. 21.

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cardioversion of atrial fibrillation: a controlled study.Br Heart J 1994; 72: 161-6. Lip GYH, Watson RDS, Singh SP. ABC of atrial fibrillation: cardioversion of atrial fibrillation. Br Med J 1996; 312: 1125. 9- Anon. Shimizu A, Centurion OA, Electrophysiological properties of the human atrium in atrial fibrillation Cardiovascular Research 54, 2002, 302–314 Stridh M, Sornmo L, Meurling CJ, Olsson SB. Characterization of Atrial Fibrillation Using the Surface ECG: TimeDependent Spectral Properties, IEEE Trans Biomed Eng 2001, 48:1. Bollman T, Mende M, Neugebauer A, Pfeiffer D. Atrial fibrillatory frecuency predicts atrial defibrillation threshold and early arrhythmia recurrence in patients undergoing internal cardioversion of persistent atrial fibrillaton. PACE 2002, 25:1179–1184. Shkurovich S, Sahakian AV, Swiryn S. Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique. IEEE Trans Biomed Eng 1998, 45:229–234. Coumel P: Paroxysmal atrial fibrillation: a disorder of autonomic tone? Eur Heart J 1994, 15(Suppl A):9-16. Vikman S, Makikallio TH, Yli-Mayry S, Pikkujamsa S, Koivisto AM, Reinikainen P, Airaksinen KE, Huikuri HV. Altered complexity and correlation properties of R–R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. Circulation 1999; 100:2079–84. Hogue CW Jr, Domitrovich PP, Stein PK, Despotis GD, Re L, Schuessler RB, Kleiger RE, Rottman JN. R–R interval dynamics before atrial fibrillation in patients after coronary artery bypass graft surgery. Circulation 1998; 98:429–34. Lake DE, Richman JS, Griffin MP, Moorman JR. Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol 2002; 283:R789–97. Richman J, Moorman J. Physiological time-series analysisusing approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000; 278:2039–2049. Pincus S, Acad A. Approximate entropy (apen) as complexity measure. Sci 2001; 954:245. Author: Institute: Street: City: Country: Email:

Raquel Cervigon Abad Universidad de Castilla-La Mancha. E. U. Politécnica Campus Universitario 16071 Cuenca Spain [email protected]

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USB Based ECG Acquisition System J. Mihel, R. Magjarevic University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia Abstract— Acquisition of ECG data from patients in Intensive Care Unit (ICU) following surgery is a vital part of cardiac disease detection and rehabilitation monitoring and therefore it is a standard function of ICU physiological monitors and monitoring systems. Recently, some authors have introduced continuous ECG monitoring and processing into cardiac disease prediction research. Such studies have to be performed on as many subjects as possible in order to acquire large enough amount of data, which imposes a necessity for simultaneous data acquisition from multiple patients. Current equipment found in a typical ICU is not designed to fulfill such demands and usually cannot be easily interfaced to a standard computer intended for signal data recording. The aim of this project was to design and develop a simple ECG acquisition system composed of a single recording and storage device (personal computer) and a sufficient number of pickup ECG amplifiers simultaneously connected to the recording device on a single bus. We chose USB for interconnecting the system because of its speed, reliability and power-supply and plug and play capabilities. USB is widely spread and every modern computer has multiple USB ports used for connecting peripheral devices and as such it fulfilled the demands of our project. In order to allow complete control of the recording device to amplifier interface we developed custom USB drivers designed for Microsoft Windows XP operating system. The system was evaluated with eight amplifiers working simultaneously and no data loss was detected. Keywords— ECG acquisition, Intensive care unit (ICU), USB, Continuous monitoring

I. INTRODUCTION In a standard Intensive Care Unit (ICU) patients are connected to ICU physiological monitors that measure some physiological parameters, especially important for a particular patient. In addition there is always a possibility to set values of physiological parameters that alarm if any of the parameters crosses the preset value. Usually, continuous recording of measured physiological parameters begins only in case of an alarm and the recording lasts only for a limited time. ICU monitors are not intended for long term, continuous recording of physiological parameters which may be a drawback in research and some clinical studies. In large hospitals different surgery departments have their own ICUs. Though such units are more suitable for

collecting patient data (both anamnesis and measured physiological parameters), it is still sometimes very difficult to collect enough data for clinical studies of a particular disease. We have started a research project aiming to find parameters in ECG that could predict atrial fibrillation after coronary artery bypass grafting (CABG). In a number of studies, different authors were trying to find reliable predictors from the patients’ ECG, particularly by studying P-wave and its relation to other ECG segments [1]. In these studies the authors were usually comparing the parameters before and after the surgery and they were using rather sophisticated medical instrumentation (12 lead ECG, vector cardiograph) for recording short periods of ECG. One of the most frequently used (measured or calculated) parameters in these studies was P-wave duration. Since P-wave duration parameter was measured using different methodology and at different times the criteria for it differed significantly from study to study (e.g. from 114ms [2] to 140ms [3]). Even though some of the studies had relatively high statistical sensitivity (73% in [4], 77% in [3], 83% in [2]), they did not result in accepting clinical procedures for atrial fibrillation prediction. We have decided to take another approach in research of AF after CABG. Instead of recording samples (short segments) of ECG at different time with sophisticated instrumentation that is not suitable for continuous recording of ECG, we have decided to continuously record the ECG from standard II ECG lead for the first 48 hours after the surgery and to search for predictors within that time. However, those 48 hours were segmented into shorter time slots (one hour) and we have measured or calculated more than 80 ECG parameters relevant for the study [5]. These parameters were statistically analyzed and after identifying statistically significant parameters, we have used them for building prediction models [6]. In this paper we described the design of a USB based ECG acquisition system we have developed for the purpose of this study. Our system is intended to be used in one ICU and enables acquisition of a single ECG channel from several patients simultaneously and all data from these patients’ ECGs is recorded on a PC. The device is simple and small and it connects the patient and the ICU monitor using standard cables while an USB cable connects the device with a laptop or a desktop PC.

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USB Based ECG Acquisition System

II. MATERIALS AND METHODS Our acquisition system consists of three main units: • • •

Pickup ECG amplifier – used for analog signal acquisition and A/D conversion. An arbitrary number of amplifiers can be working at the same time in the system. PC Host – used for recording data received from piickup amplifiers. Intended to be only one in the system, i.e. per ICU. USB – used to connect the PC Host and pickup amplifiers and to transfer data from the amplifiers to the PC host.

System was intended to connect several patients in a standard ICU to a single PC Host located somewhere in the ICU itself. Standard working order of the system is to have one pickup amplifier per patient each connected to a USB hub which is connected to a PC Host used as a data recording device. All amplifiers are to be powered from the USB alone, so no additional power source is necessary (this reduces the need for additional wiring in the ICU). System overview is given is given in Fig. 1:

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ler over a serial bus (SPITM bus) and is further sent over the USB to the PC Host. USB communication protocol was implemented on a Microchip’s PIC18F/LF4550 microcontroller which we chose for its USB V2.0 compliance, Full Speed USB transfer support, embedded USB Serial Interface Engine (SIE) that enables easy USB communication and an on-chip USB transceiver and 3.3V voltage regulator. Pickup amplifier was designed to receive the ECG signal from a standard n-pin ECG connector. Input ECG signal is further transferred to an m-pin standard ECG connector (m ≥ n) to be connected to a standard ICU monitor used for patient monitoring. One of three standard ECG leads is connected to the input of the acquisition and A/D conversion hardware and is further amplified, sampled and as such transferred to a PC Host over the USB. Design of a pickup ECG amplifier can be depicted with a simplified block diagram shown in Fig. 2. Data is transferred from the microcontroller to a PC host using one USB input interrupt endpoint (Endpoint 1) and is formed in multiples of 3 byte packets. Each 3 byte packet contains a 2 byte (16 bit) sampled signal value and 1 byte representing PGA gain at the moment of sampling. Interrupt data transfer was chosen for its reliable and timely delivery of data. Interrupt interval was chosen to be 2 ms to ensure that the sampled data would be transferred as soon as possible without noticeable delays (since the sampling frequency was designed to be 500 Hz). Along with the implementation of required USB control transfer using Endpoint 0 (response to standard USB setup requests), one custom request was implemented for initiating the amplifier’s calibration process. Universal Serial Bus firmware implemented on the PIC18F/LF4550 microcontroller was developed to be compliant to the USB V2.0 specification requirements [7] (Chapter 9 – “USB Device Framework”). USB data transfer operates at Full Speed (12 Mbit/s) and given that a pickup amplifier consumes up to 70 mA each one is fully powered from the USB alone and does not require an additional power source.

Fig. 1 Acquisition system overview Pickup amplifier is intended for signal acquisition as a single channel ECG amplifier using one of three standard ECG leads as input. Input signal is sampled with 500 Hz sampling frequency and 16 bit resolution. We have anticipated the possibility to set four different gain values (200, 500, 1000 and 2000) to enable better effective A/D conversion resolution. Programmable gain is automatically adjusted dependently of the input signal level. In case of superimposition of a great number of artifacts to the ECG signal the gain changes to a smaller value to prevent amplifier saturation. Following A/D conversion sampled data is transferred to a microcontrolFig. 2 Pickup ECG amplifier block diagram

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Custom Windows drivers were developed using Microsoft Kernel-Mode Driver Framework (KMDF) and they conform to Microsoft’s plug and play and power management requirements. Drivers are compatible with and were evaluated on Windows 2000 and Windows XP operating systems, and will be further evaluated for their compatibility with Windows Vista operating system. Using simple Win32 API calls a Windows application can enumerate all amplifiers plugged into the system, and retrieve data from each one of them. Double buffering – buffering on both the microcontroller and the Windows USB driver ensures that a sufficient number of pick up ECG amplifiers can be connected into the system at the same time without any data loss on the bus. Double buffering was necessary to insure that even a slower Windows application on the PC Host could properly and in timely fashion acquire data from a desired amplifier connected to the system. Data buffer on a pickup amplifier was set to be 63 bytes (maximum USB packet size for full speed interrupt transfers is 64 bytes) which enables buffering of 21 3 byte data packets and the buffer in the device driver was set to be 4095 bytes (1365 3 byte data packets). PC Host to pickup amplifier communication can be described with the simplified block diagram shown in Fig. 3.

and identification of all pick up amplifiers connected to a USB hub, and for recording of data received from each amplifier in a separate file. All amplifiers connected to the PC Host were properly identified via their serial numbers and accessed from the application. Evaluation lasted 48 hours in which no data loss was detected from any of the eight amplifiers. For purposes of the CABG project we developed several pickup ECG amplifiers to be used in the ICU of the Department of Cardiac Surgery of the Clinical Hospital Center in Zagreb. Amplifiers were constructed to be used for acquisition from the II. standard ECG lead but can be easily modified to accept any of the two other leads as input. Use of standard ECG connectors on our device facilitates uninterrupted work and standard procedures in the ICU and high acceptance by medical staff. Each pickup amplifier is embedded into a robust housing suitable for use in ICU. Fig. 4 shows a pickup ECG amplifier intended for use in the Clinical Hospital Center in Zagreb. For purposes of evaluating quality of signal acquisition and processing on a pickup amplifier we designed a simple Win-

III. RESULTS System was evaluated by simultaneously connecting eight amplifiers to a single PC Host. For the purposes of this evaluation a simple Win32 application was developed. This application was responsible for enumeration

Fig. 4 pickup ECG amplifier – a) top view, b) front panel view, c) back panel view

Fig. 3 Simplified block diagram of USB communication

Fig. 5 ECGView test application

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USB Based ECG Acquisition System

dows application called ECGView that enables monitoring of the received data on the screen of the PC Host. Application was designed using Microsoft .NET Framework 2.0, C# 2.0 programming language and DirectX Software Development Kit. We evaluated the received signal and found it satisfying for purposes of monitoring and data acquisition. Further noise filtering (if needed) is to be done in an application that processes the received data and extracts information from the recorded signal.

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REFERENCES 1. 2. 3. 4.

IV. CONCLUSIONS We have developed a simple ECG acquisition system intended for continuous recording of patients’ ECG in ICUs. The system is implemented at the Department of Cardiac Surgery of the Clinical Hospital Center in Zagreb in research of atrial fibrillation prediction after coronary artery bypass grafting. The system can be implemented in any other research application which needs long term continuous single channel ECG signal recording and its on line processing on personal computer. Our system is easily implemented in an ICU and does not disrupt its usual work flow. Use of USB and custom device drivers enables easy connection of pickup amplifiers to a PC Host running Windows operating system.

5. 6. 7.

Clavier L, Boucher JM, Lepage R, Blanc JJ, Cornily JC (2001) Automatic P-wave analysis of patients prone to atrial fibrillation. Medical & Biological Engineering and Computing, 40: 63-71 Buxton AE, Josephson ME (1981) The role of P-wave duration as a predictor of postoperative atrial arrhythmias. Chest, 80: 68-73 Steinberg JS, Zelenkofske S, Wong SC, Gelernt M, Sciacca R, Menchavez E (2000) Value of the P-wave signal-averaged ECG for predicting atrial fibrillation after cardiac surgery. Europace 2(1):32-41. Stafford PJ, Kolvekar S, Cooper J, Fothergill J, Schlindwein F, deBono DP, Spyt TJ, Garratt CJ (1997) Signal averaged P-wave compared with standard electrocardiography or echocardiography for prediction of atrial fibrillation after coronary bypass grafting. Heart 77: 417-422 Sovilj S, Rajsman G, Magjarevic R. (2005) Continuous Multiparameter Monitoring of P Wave Parameters after CABG Using Wavelet Detector. Proc. Computers in Cardiology; 32:945-8. Sovilj S, Rajsman G, Magjarevic R. (2006) Multiparameter Prediction Model for Atrial Fibrillation after CABG. Detector. Proc. Computers in Cardiology; 33:489-92. Universal Serial Bus Specification, Revision 2.0 (2000) Address of the corresponding author: Author: Ratko Magjarevic Institute: University of Zagreb, Faculty of Electrical Engineering and Computing Street: Unska 3 City: Zagreb Country: Croatia Email: [email protected]

ACKNOWLEDGMENT This study was supported by Ministry of Science, Education and Sport of the Republic of Croatia under grant no. 036-0362979-1554.

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Using Supervised Fuzzy Clustering and CWT for Ventricular Late Potentials (VLP) Detection in High-Resolution ECG Signal Ayyoub Jafari, M.H. Morradi Biomedica Engineering Department Tehran Polytechnic University.Iran [email protected],[email protected] Abstract— Ventricular Late Potentials (VLPs) are lowamplitude, high-frequency signals that appear at the end of the QRS complex of a High-Resolution ECG (HRECG) records. VLPs are clinically useful for identifying post-MI (Myocardial Infarction) patients prone to Ventricular Tachycardia (VT) and Sudden Cardiac Death (SCD). In this paper, the Continuous Wavelet Transform (CWT) and a supervised fuzzy clustering algorithm are used together to detect VLPs. The terminal part of the QRS complex in the Vector Magnitude (VM) waveform is processed with the CWT to extract a feature vector. Resulting time-scale representation is subdivided into several sub bands, and the sum of the squared decomposition coefficients is computed in each region. Finally, a supervised Fuzzy clustering method, trained by an appropriate set of these feature vectors, is applied to this data in order to identify VLP. Keywords— VLP-Fuzzy Clustering-HRECG

I. INTRODUCTION Most of the Sudden Cardiac Deaths (SCDs) due to cardiac diseases are thought to be initiated by Ventricular Tachycardia (VT) which is one of the most serious types of cardiac arrhythmia [11]. As the appearance of Ventricular Late Potentials (VLPs) is associated with VT, there is a clinical interest in detection of these signals as a noninvasive diagnosis for post-MI (Myocardial Infarction) patients prone to VT. VLPs are low-amplitude, highfrequency signals which appear at the end of the QRS complex and arise as a result of the late depolarization of damaged myocardium. Because of their very low amplitudes and the noise overlay, VLPs are obscure in a standard electrocardiogram (ECG) but they can be detected by a High-Resolution ECG (HRECG) record acquired using three orthogonal XYZ leads with a minimum sampling frequency of 1000Hz and a resolution of 12 bits [1,9,11]. There are various techniques to improve signal-to-noise ratio (SNR) in VLP analysis. Typically several heart beats (200-300) are averaged to suppress the background noise and form the Signal Averaged ECG (SAECG). In this paper, a new fuzzy model structure is proposed for the VLP classification problem in which each rule can represent more than one class with

different probabilities. Typical fuzzy classifiers consist of interpretable if-then rules with fuzzy antecedents and class labels in the consequent part. The antecedents (if-parts) of the rules partitions the input space into a number of fuzzy regions by means of fuzzy sets, while the consequents (thenparts) describe the output of the classifier in these regions. Fuzzy logic improves rule-based classifiers by allowing the use of overlapping class definitions and improves the interpretability of the results by providing more insights into the decision making process. The automatic determination of compact fuzzy classifiers rules from data has been approached by several different techniques: neuro-fuzzy methods, genetic-algorithm (GA) based rule selection, and fuzzy clustering in combination with GA-optimization. Generally, the bottleneck of the data-driven identification of fuzzy systems is the structure identification that requires nonlinear optimization. Thus for high-dimensional problems, the initialization of the fuzzy model becomes very significant. Common initialization methods such as grid-type partitioning [26] and rule generation on extreme initialization, result in complex and non-interpretable initial models and the rule-base simplification and reduction steps become computationally demanding. To avoid these problems, fuzzy clustering algorithms [37] were put forward. However, the obtained membership values have to be projected onto the input variables and approximated by parameterized membership functions that deteriorate the performance of the classifier. This decomposition error can be reduced by using eigenvector projection [28], but the obtained linearly transformed input variables do not allow the interpretation of the model. To avoid the projection error and maintain the interpretability of the model, the proposed approach is based on the Gath-Geva (GG) clustering algorithm [22] instead of the widely used Gustafson-Kessel (GK) algorithm [38], because the simplified version of GG clustering allows the direct identification of fuzzy models with exponential membership functions [25]. Neither GG nor GK algorithm utilizes the class labels. Hence, they give suboptimal result if the obtained clusters are directly used to formulate a classical fuzzy classifier.

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Ayyoub Jafari, M.H. Morradi

Filtered QRS QRST Amplitude (μV)

Hence, there is a need for fine-tuning of the model. This GA or gradient-based fine-tuning, however, can result in overfitting and thus poor generalization of the identified model. Unfortunately, the severe computational requirements of these approaches limit their applicability as a rapid model-development tool. This paper focuses on the design of interpretable fuzzy rule based classifiers from data with low-human intervention and low-computational complexity. Hence, a new modeling scheme is introduced based only on fuzzy clustering. The proposed algorithm is similar to the Multi-Prototype Classifier technique [18, 33]. The main difference of this approach is that each cluster represents different classes, and the number of clusters used to approximate a given class has to be determined manually, while the proposed approach does not suffer from such problems. Generally, there is a very large set of possible features to compose feature vectors of classifiers. As it is ideal that the training dataset's size should increase exponentially with the feature vector size, it is desired to choose a minimal subset among it. Some generic tips to choose a good feature set include the facts that they should discriminate as much as possible the pattern classes and they should not be correlated or redundant. The paper organization is as follows. In section II, the Simson method is described. Section III will describe the feature extraction process. Section IV will take the developed clustering algorithm into consideration that allows the direct identification of fuzzy classifiers. For selecting the most important features of the fuzzy system, a method based on Fisher interclass separability criteria will be presented in section V. Section VI presents the results of applying this method to a HRECG database. Finally, some conclusions and discussions will be made in section VII.

V40 (area) Offset Time (ms)

Fig. 1 A typical filtered QRS complex and the definition of three conventional time-domain features: QRST, D40 and V40.

conventional time-domain features can be measured to detect VLPs [1, 11, 12, 13]: -

QRST: Duration of the filtered QRS complex (from the onset to the offset) D40: Low-amplitude signal duration (from the offset backward to the point where VM reaches the 40μV) V40:Root-mean-square value of the last 40ms of the filtered QRS (showed in Fig. 1)

The criteria to define a VLP positive test are QRST >114ms, D40>38ms and V40<20μV [11]. In Fig.1, a plot of a typical filtered QRS complex and the definition of the conventional time-domain features, introduced above, can be viewed. III. FEATURE EXTRACTION

The conventional time-domain method of VLP detection, developed by Simson [8], is based on feature extraction from the filtered SAECG [8, 9, 13]. Simson's method employs a high-pass filter (cutoff frequency of 25 or 40Hz) to attenuate low-frequency components of averaged XYZ signals (SAECG). To avoid the filter ringing effect in the terminal parts of QRS complex, Simson proposed a bi-directional four-pole Butterworth high-pass filter [8]. After high-pass filtering of the averaged XYZ signals, these signals are combined into a Vector Magnitude (VM) waveform defined by X 2 +Y 2 + Z2

40ms Onset

II. SIOMSON METHOD

VM =

D40

(1)

After estimating the onset and offset of the filtered QRS complex (the QRS complex in the VM signal), three

The adopted feature extraction method for VLP consists of 5 stages: -

-

Averaging XYZ leads to improve the SNR Bidirectional Butterworth filtering of averaged XYZ signals consisting of a 4th order high-pass and a 5th order low-pass filters with cutoff frequencies of 40Hz and 250 Hz respectively. Combination of the filtered averaged signals into a VM waveform using equation (1). Applying the CWT to the terminal part of the QRS complex in the VM signal. Feature extraction from the resulted time-scale plot.

In recent years, the wavelet analysis has been used widely in biomedical researches [2, 5, 7, 14]. The wavelet transform is a linear time-scale transform which is based on

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Using Supervised Fuzzy Clustering and CWT for Ventricular Late Potentials (VLP) Detection in High-Resolution ECG Signal

decomposition of a signal using a set of basis functions. These basis functions are scaled and shifted versions of a prototype mother wavelet [4, 11]. The CWT produces a time-scale representation of the signal which is a function of time τ and scale a, like CWT (τ, a) named wavelet decomposition coefficient. The scale can be considered as the inverse of the frequency [9]. The smaller scales bring about a higher resolution in time which is useful to detect VLPs as high-frequency, shortduration signals. In this study, the CWT is adopted, using the MATLAB wavelet toolbox, to the last 40ms of the QRS complex in the VM signal after estimating the offset point. To improve the robustness of the method to the error in QRS offset detection, a 8ms right shift is considered for the estimated offset [13] so that the CWT is applied to 48ms interval of the VM waveform, with scale range of 1-8 and the Morlet wavelet as the mother wavelet [6]. Then, the resulted time-scale representation is subdivided into 81 regions [6] (nine subdivisions on both the time and scale axes). Finally, the sum of the squared wavelet decomposition coefficients is computed in each region to form a feature vector composed of the 81 elements. IV. SUPERVISED FUZZY CLUSTERING The objective of clustering is to partition the identification data Z into R clusters. This means, each observation consists of input and output variables grouped into a row vector Zk = [xTk yk], where the k subscript denotes the k = 1… Nth row of the pattern matrix of Z. The fuzzy partition is represented by the U= [μi,k] R*N matrix, where the μi,k element of the matrix represents the degree of membership, how the Zk observation is in the cluster i = 1…R. The clustering is based on the minimization of the sum of weighted D2i, k squared distances between the data points and the ηi cluster prototypes that contains the parameters of the clusters. R N ⎡ ⎤ = J ( Z , U , η ) ( μ i ,k ) m D 2 ( z k , ri ) ⎥ ∑∑ ⎢ i =1 k =1 ⎣ ⎦

(2)

In which, m is the fuzzy weighting exponent that determines the fuzziness of the resulting clusters. Usually, the chosen value for m will be m = 2. Classical fuzzy clustering algorithms are used to estimate the distribution of the data. Hence, they do not utilize the class label of each data point available for the identification. Furthermore, the obtained clusters cannot be directly used to build the classifier. In the following a new cluster prototype and the related distance measure will be introduced that allows the direct supervised identification of fuzzy classifiers. As the

101

clusters are used to obtain the parameters of the fuzzy classifier, the distance measure is defined similarly to the distance measure of the Bayes classifier: 1 = P(ri ) D (zk , ri ) 2 i,k

1 (xj,k − vi, j )

n

2

∏ exp (− 2

σ

j =1

2 i, j

) P(cj = yk ri )

(3)

This distance measure consists of two terms; the first term is based on the geometrical distance between the vi cluster centers and the xk observation vector, while the second is based on the probability that the ri-th cluster describes the density of the class of the k-th data, P (cj = yk | ri) It is interesting to note, that this distance measure only slightly differs from the unsupervised Gath–Geva clustering algorithm which can also be interpreted in a probabilistic framework [21]. However, the novelty of the proposed approach is the second term, which allows the use of class labels. To get a fuzzy partitioning space, the membership values have to satisfy the following conditions: U ∈ Rc×N μi,k ∈[0,1], ∀i, k ;

R

∑μ i=1

i ,k

N

= 1 , ∀k ; 0 < ∑μi,k < N , ∀i (4) k =1

The minimization of the (6) functional represents a nonlinear optimization problem that is subject to constraints defined by (5) and can be solved by using a variety of available methods. The most popular method, is the alternating optimization (AO), which consists of the application of Picard iteration through the first-order conditions for the stationary points of (6), which can be found by adjoining the constraints (5) to J by means of LaGrange multipliers [24], R

−

N

N

R

k =1

j =1

J (Z ,U ,η, λ) = ∑∑(μi,k )m D2 (zk , ri ) + ∑λk (∑ μi,k − 1) i=1 k =1

(5)

And by setting the gradients of J to zero, with respect to Z, U, η and λ. Hence, similarly to update the Gath-Geva clustering algorithm equations, the following equations will result in a solution that satisfies the (6) constraints. Initialization: Given a set of data Z specify R, choose a termination tolerance ε>0. Initialize the U= [μi,k] RεN partition matrix randomly, where μi,k denotes the membership that the Zk data is generated by the i'th cluster. Repeat for l = 1, 2… . Step1: Calculate the parameters of the clusters. Calculate the centers and standard deviation of the Gaussian membership functions (the diagonal elements of the Fi covariance matrices) N

v

(l ) i

=

∑ (μ k =1 N

( l −1) m i ,k

∑ (μ k =1

) xk

( l −1) m i ,k

)

N

, σ

2(l ) i, j

=

∑ (μ k =1

( l −1) m i ,k

) ( x j ,k − v j ,k ) 2

N

∑ (μ k =1

(6)

( l −1) m i ,k

)

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Estimate the consequent probability parameters.

p( ci rj ) =

∑ ∑

k yk =ci N

k =1

(μ (jl,k−1) )m

(μ

(l −1) m j ,k

)

, 1 ≤ i ≤C, 1 ≤ j ≤ R

(7)

A priori probability of the cluster and the weight (impact) of the rules. P (ri ) =

1 N

N

∑ (μ k =1

( l −1) m i ,k

)

n

, wi = P (ri )∏ j =1

1

(8)

2πσ i2, j

Step2: Compute the distance measure D2i, k by (4). Step3: Update the partition matrix μi(,lk) =

1 R

∑ (D

i,k

j =1

, 1≤ i ≤ R , 1 ≤ k ≤ N

( zk , ri ) / D j , k ( zk , rj )) 2 /( m −1)

untill U ( l ) − U ( l −1) 〈ε

(9)

(10)

V. FEATURE SELECTION Using too many input variables may result in difficulties in the interpretability capabilities of the obtained classifier. Hence, selection of the relevant features is usually necessary. In this paper, the modified Fischer interclass separability method is used which is based on statistical properties of the data. The interclass separability criterion is based on the FB between-class and the FW within class covariance matrices that sum up to the total covariance of the data FT=FB+FW, where: FW = v0 =

R

∑

l =1

P ( rl ) F l , F B =

R

∑

l =1

P ( r l ) ( v l − v 0 ) T ( v l − v 0 ),

(10)

R

∑

l =1

P ( rl ) v

(11 )

l

J=

det ( FB ) det ( Fw )

(11)

The feature interclass seperatibility selection criterion is a trade-off between FW and FB (Eq.12). The importance of a feature is measured by leaving out the interested feature and calculating J for the reduced covariance matrices. The feature selection is a step-wise procedure, when in every step the least needed feature is deleted from the model [29]. VI. RESULTS To evaluate the method used in this study a HRECG database consisting of two groups of signals was selected. The first group contained 50 healthy volunteers’ HRECG records acquired by a digital data acquisition system,

ML785 PowerLab/8SP, with a sampling frequency of 2000Hz and a 16-bit analog-to-digital converter (ADC). The second group was consisted of semi-simulated HRECG signals with VLPs. In order to simulate each of these signals, three basic simulated waveforms resembling the VLP characteristics were added to XYZ leads of a basic HRECG record, a HRECG record without VLP. VLPs are low-amplitude signals (~1-20μV) with short duration (~550ms) and broadband spectrum (~40-250Hz) [11]. According to these characteristics, VLPs were simulated as colored Gaussian processes resembling better the real world signals. The basic VLP waveforms were added to the end part of the QRS complex of every heart beat of the XYZ leads belonging to the basic HRECG records. The position of the VLPs was varied randomly from beat to beat with respect to the fiducial mark, QRS peak [10, 11]. This HRECG database was divided into a training set, including thirty HRECG signals with VLPs and thirty without, and a test set consisting of 20 records without VLPs and 20 with. For a better training of the neural network and preserving its generalization, the training set was expanded; five sets containing 300 heart beats were selected from every HRECG record of training set randomly. Because of the fact that every HRECG record had at least 350 heart beats, the beat selection was done without replacement for each set. Therefore, an expanded training set consisting of 300 patterns was obtained. The performance of the VLP detection method was measured using conventional criteria i.e. the accuracy ACC, sensitivity SE, and specificity SP defined by ACC = 100 × (TP + TN ) / N

SE = 100 × TP /(TP + FN )

SP = 100 × TN /(TN + FP )

(12)

(13)

Where N, TP, TN, FP, and FN are respectively the total number of patterns, the number of true positive, the number of true negative, the number of false positive, and the number of false negative [6]. Using the expanded training set and the test set, the method based on the CWT and the Fuzzy Supervised Clustering system, introduced in Fig.2, was evaluated that showed good results for the test set. To investigate the performance of the VLP detection method proposed in this work, the conventional time-domain method (Simson's method) was applied to the test set; also, a method based on applying our system to the conventional time-domain features [4,13] was used to detect VLP. Table1 presents the results of the proposed method in comparison with Simson's method and applying a Proposed Fuzzy System to the conventional time-domain features, for the test set. In Simson's method, the balance between SE and SP can be controlled by choosing one, two, or three of the positive VLP criteria (QRST>114ms, D40>38ms, and V40<20μV) at the same time. For example, if higher SE is

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Using Supervised Fuzzy Clustering and CWT for Ventricular Late Potentials (VLP) Detection in High-Resolution ECG Signal

103

ACC( %)

SE( %)

SP( %)

• One Criterion

80

100

60

• Two Criterion

75

80

70

• Three Criterion

72

60

85

offset, and even a few errors in estimating of this point can result in wrong diagnostic. However the method used in this study is robust to the error in QRS offset detection. The results represented in Table 1 shows that the CWT based features, used in this study, are more capable than the conventional time-domain features to detect VLPs using Supervised Fuzzy Clustering Systems. According to the above advantages, the proposed method may be an appropriate alternative method for the clinical detection of VLP, if it is evaluated completely. Due to the lack of real HRECG for VLP in this research, the proposed method must be applied to a larger HRECG database consisting of real signals with and without VLPs to complete the evaluation. Form Table1, Neural Network Method is a little better than Our Supervised Clustering method but our system is based on only five fuzzy rule and so the training of system is much faster than a MLP Neural Network and can be used in practical applications.

92.5

91

94

REFERENCES

• Applying Supervised Fuzzy Clustering to the conventional time-domain features

82

81. 26

83. 66

• Applying Supervised Fuzzy Clustering to VM_QRS40 Features

84

73. 76

94. 13

• Applying Supervised Fuzzy Clustering to mVM_QRS40 Features

90

89. 88

91. 16

• Applying Supervised Fuzzy Clustering to mVM_CWT1 Features

92

88. 83

95. 16

desired, only one of the three criteria is chosen; in contrast, all of the criteria must be satisfied at the same time to achieve higher SP. This fact can be seen in Table 1. In most cases, two of the three criteria are used to obtain the balance between SE and SP [13]. These results obtained with a Fuzzy system with 5 rules. • Table1: VLP detection results. The comparison between the proposed method, Simson's method and applying a Supervised Fuzzy Clustering system to the features (for the test set).

Method

• Simso n's Method

• Applying Neural Network Method to mVM_CWT1 Features

VII. CONCLUSION The aim of this work was to investigate the capability of a method based on the continuous wavelet transform and a Supervised Fuzzy Clustering, in order to extract features and classify for VLP detection problem in HRECG. The results show good improvements in sensitivity and discriminancy compared to Simson's method and Supervised Fuzzy Clustering to the conventional time-domain features (see Table1). Another possible advantage of the proposed method can be the ability of the VLP detection in patients with bundle branch block, while Simson's method can not be used with these patients because the bundle branch block causes the QRS duration to be extended and consequently increases QRST. Simson's method is very sensitive to QRS

[1] I.C.Baykal and A.Yilmaz, Detection of Late Potentials in Electrocardiogram Signals in both Time and Frequency Domains Using Artificial Neural Networks, 44th IEEE Midwest Symposium on Circuits & Systems, 2001, pp.576-579. [2] S.W.Chen, A Wavelet-Based Heart Rate Variability Analysis for the Study of Nonsustained Ventricular Tachycardia, IEEE Transactions on Biomedical Engineering, Vol.49, No.7, 2002, pp.736-742. [3] A.Cohen , Biomedical Signal Processing, CRC Press, VolumeII, 1986. [4] A.Mousa and A.Yilmaz, Neural Network Detection of Ventricular Late Potentials in ECG Signals Using Wavelet Transform Extracted Parameters, IEEE Proceedings of the 23rd Annual EMBS International Conference, Turkey, 2001, pp.1668-1671. [5] A.Mousa and A.Yilmaz, A Method Based on Wavelet Analysis for the Detection of Ventricular Late Potentials in ECG Signals, 44th IEEE Midwest Symposium on Circuits & Systems, 2001, pp.497-500 . [6] A.Rakotomamonjy, B.Migeon, and P.Marche, Automated Neural Network Detection of Wavelet preprocessed Electrocardiogram Late Potentials, Med.Biol.Eng.Comput.,Vol.36, 1998, pp.346 -350. [7] M.B.Simson, Use of Signals in The Terminal QRS Complex to Identify Patients with Ventricular Tachycardia after Myocardial Infarction, Circulation, Vol.64, No.2, 1981, pp. 235-242. [8] A.Spaargaren and M.J.English, Detecting Ventricular Late Potentials Using the Continuous Wavelet Transform, Proceedings of Computers in Cardiology,IEEE Comput.Soc.,Vol.26, 1999, pp.5-8. [9] A.Taboada-Crispi, J.V.Lorenzo-Ginori, and D.F.Lovely, Adaptive Line Enhancing Plus Modified Signal Averaging for

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104 Ventricular Late Potential Detection, Electronics Letters, Vol.35, No.16, 1999, pp.1293-1295. [10] A.Taboada-Crispi, Improving Ventricular Late Potentials Detection Effectiveness, Doctoral Thesis, University of New Brunswick,2002. [11] S.Wu, Y.Qian, Z.Gao, and J.Lin, A Novel Method for Beat-toBeat Detection of Ventricular Late Potentials, IEEE Transactions on Biomedical Engineering,Vol.48,No.8,2001, pp.931-935. [12] Q.Xue and B.R.S.Reddy, Late Potential Recognition by Artificial Neural Networks, IEEE Transactions on Biomedical Engineering, Vol.44, No.2, 1997, pp.132-143. [13] Janos Abonyi and Ferenc Szeifert, Supervised Fuzzy Clustering for the Identification Of Fuzzy Classifiers , University of Veszprem, Dep. of Process Engineering, [14] Detection of Ventricular Late Potentials in High-Resolution ECG Signals by a Method Based on the Neural Network and Continuous Wavelet Transform , Ali Shahidi Zandi , Mohammad Hassan Moradi [15] Baraldi A. and Blonda P. (1999) A survey of fuzzy clustering algorithms for pattern recognition - Part I, IEEE Transaction On Systems, Man and Cybernetics Part B29 (6): 778-785. [16] Bezdek J.C., Hathaway R.J., Howard R.E., Wilson C.A. and Windham M.P., (1987) Local Convergence Analysis of a Grouped Variable Version of Coordinate Descent. Journal of Optimization Theory and Applications, 71:471–477. [17] Biem A., Katagiri S., McDermott E., Juang BH. (2001) An application of discriminative feature extraction lo filter-bankbased speech recognition.IEEE Transactions On Speech And Audio Processing 9 (2): 96-110. [18] Campos T. E., Bloch I., Cesar R. M. Jr. (2001) Feature Selection Based on Fuzzy Distances Between Clusters: First Results on Simulated Data.Lecture Notes in Computer Science, Springer-Verlag press, ICAPR’2001 - International Conference on Advances in Pattern Recognition, Rio de Janeiro, Brazil, May.16 [19] Cios K.J., Pedrycz W., Swiniarski R.W. (1998) Data Mining Methods forKnowledge Discovery. Kluwer Academic Press, Boston.[20] Corcoran A.L., Sen S. (1994) Using real-valued genetic algorithms to evolve rule sets for classification. In IEEE-CEC, June 27-29, 120–124, Orlando, USA. [20] Gath I. and Geva. A.B. (1989) Unsupervised Optimal Fuzzy Clustering,IEEE Transactions on Pattern Analysis and Machine Intelligence 7 773–781. [21] Gustafson, D.E., Kessel, W.C. (1979) Fuzzy Clustering With a Fuzzy Covariance Matrix, In Proc. IEEE CDC, San Diego, USA. [22] Hathaway R.J. and Bezdek J.C. (1993) Switching Regression Models and Fuzzy Clustering. IEEE Transactions on Fuzzy Systems, 1:195–204.

Ayyoub Jafari, M.H. Morradi [23] Hoppner F., Klawonn F., Kruse R. and Runkler T. (1999) Fuzzy Cluster Analysis – Methods for Classification, Data Analysis and Image Recognition,John Wiley and Sons. [24] Ishibuchi H., Nakashima T., Murata T. (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems.IEEE Trans. SMC–B 29, 601–618. [25] Kambhatala N. (1996) Local Models and Gaussian Mixture Models for Statistical Data Processing, Ph.D. Thesis, Oregon Gradual Institute of Science and Technology. [26] Kim E., Park M., Kim S. and Park M. (1998) A Transformed Input–Domain Approach to Fuzzy Modeling. IEEE Transactions on Fuzzy Systems, 6:596–604. [27] Loog L.C. M., Duin R.P.W. ,Haeb-Umbach R. (2001) Multiclass linear dimension reduction by weighted pairwise Fisher criteria, IEEE Trans. On PAMI, vol. 23, no. 7, pp. 762-766. [28] Nauck D. and Kruse R. (1999) Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine, 16: 149–169. [29] Pe˜na-Reyes C. A. and Sipper M. (2000) A fuzzy genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine, 17: 131–155. [30] Quinlan J. R. (1996) Improved Use of Continuous Attributes in C4.5, Journal of Artificial Intelligence Research, 4: 77–90. [31] Rahman A.F.R. and Fairhurst M.C. (1997) Multi-prototype classification: improved modelling of the variability of handwritten data using statistical clustering algorithms. Electron. Lett. 33 14, pp. 12081209.17 [32] Roubos J.A., Setnes M. (2000) Compact fuzzy models through complexity reduction and evolutionary optimization. In FUZZ-IEEE, pp762-767, May 7-10, San Antonio, USA. [33] Roubos J.A., Setnes M. and Abonyi J. (2001) Learning fuzzy classification rules from data. Developments in Soft Computing, eds. John, R. and Birkenhead, R., Springer Verlag Berlin/Heidelberg, 108-115. [34] Setiono R. (2000) Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine, 18: 205–219. [35] Setnes M., Babuˇska R. (1999) Fuzzy Relational Classifier Trained by Fuzzy Clustering, IEEE Trans. SMC–B, 29, 619– 625 [36] Setnes M., Babuˇska R., Kaymak U., van Nauta Lemke H.R. (1998) Similarity measures in fuzzy rule base simplification. IEEE Trans. SMC–B 28, 376–386. [37] Takagi T., Sugeno M. (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans. SMC 15, 116–132. [38] Valente de Oliveira J. (1999) Semantic constraints for membership function optimization. IEEE Trans. FS 19, 128–138.

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An Approach to the Real-Time Surface Electromyogram Decomposition V. Glaser, A. Holobar and D. Zazula University of Maribor, Slovenia Abstract— This paper studies a sequential decomposition method suitable for real-time separation of linear mixtures of finite-length signals. The signals are modelled as channel responses in a multiple-input multiple-output (MIMO) model, with positive pulse trains as channel inputs. Our decomposition method compensates the channel responses and aims to reconstruct the input pulse trains in real time. Tests on synthetic surface electromyograms (SEMG) show how well the proposed method performs, in comparison to its batch version, and how robust it is. Keywords— Compound signal decomposition, Surface electromyogram, Real-time signal processing, Sherman-Morrison matrix inversion, Sequential convolution kernel compensation

I. INTRODUCTION Linear modelling of multiple observations excited by multiple input source signals proved to be the right answer for signal decomposition in many practical cases. We meet this approach in the fields of biomedicine, radar, sonar, image processing, etc. Several established methods for monitoring and diagnosing the human muscle and nerve system make use of the electromyography (EMG). Special electrodes detect the electrical muscle or nerve responses called action potentials (AP). These responses accompany muscle contractions that are caused by the potentials travelling along the muscle fibres. A bunch of muscle fibres is bound into a muscle unit, the so called motor unit (MU). Every motor unit is innervated and excited by a train of electric pulses that arrive through the nerves from the spinal cord and brain [4]. For practical reasons, the EMG signals are recently more and more measured by surface electrode (surface EMG). This means highly superimposed signals where the contributions of individual motor-unit action potentials (MUAPs) sum up into a compound signal [4]. A real EMG diagnostic value, whose application has been paved by the tradition of the needle EMG measurements, is based on the knowledge about its constituent components observed through individual MUAPs. Therefore, a strong need for reliable, robust and fast methods for SEMG decomposition has emerged recently. One of new promising approaches was developed in the System Software Laboratory at the University of Maribor and was called Convolution Kernel Compensation (CKC) [1]. Its original derivation utilises the correlation

matrix built out of several simultaneous measurements that are processed in rather long segments. Hence, the underlying process of this approach is batch, which does not meet the requirement for a fast decomposition solution. In this paper, we are going to explain how the batch CKC version can be upgraded into a real-time processing algorithm. It will be derived as a sequential CKC. In Section II, a brief summary of the batch version of CKC is provided, followed by the derivation details for its sequential version in Section III. Both the CKC and sequential CKC are quite noise resistant, which is shown by simulations in Section IV. Section V concludes the paper. II. DATA MODEL AND THE IDEA OF CONVOLUTION KERNEL COMPENSATION

Resume briefly the CKC basics from [1]. A MIMO model of SEMG assumes M different observations xi = { xi ( n); n = 0,1, 2,...} ; i = 1,..., M , each N samples long: xi (n ) =

N

L −1

∑∑

j =1 l = 0

h ij ( l ) s j ( n − l );

(1)

i = 1 ,..., M ,

where hij (l ) is a response of the j-th source with L samples, appearing in i-th observation, while s j (n − l ) stands for a positive pulse train from the j-th source. When noisy observations are considered, (1) extends to: y i ( n ) = x i ( n ) + ω i ( n );

i = 1,..., M ,

(2)

where additive noise ω i is considered as a stationary, zeromean Gaussian random process. To assure that EMG model is over-determined, the model is extended with K-1 delayed repetitions of each observation: y ( n ) = ⎡⎣ y1 ( n ) , y1 ( n − 1) ,...., y1 ( n − K + 1) ,...., y M ( n ) ,...., y M ( n − K + 1) ⎤⎦ . T

(3)

Adopting the model from (2) and notations from Eq. (3), the so called activity index can be calculated as follows: γ ( n ) = y T ( n ) C #y y y ( n ) = s ( n ) C −s s1 s ( n )

,

(4) T

−1

where # stands for pseudoinverse, and for matrix transpose and inverse, respectively, C yy is the correlation matrix of noisy observations y i , and C s s the correlation matrix of noise-free source signals, si . Activity index γ (n)

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actually indicates a global pulse-train activity. Suppose now we fix the premultiplying vector in (4) to the position n0, y (n 0 ) , and write it in the following form: ν n 0 ( n ) = y T ( n 0 ) C #y y y ( n )

.

(5)

It was proved in [1] that when n0 is selected such that s j (n 0 ) = 1 ,

(6)

and the j-th source is the only one active in position n0, v n0 (n) from Eq. (5) equals v n0 ( n ) = s j ( n ) .

(7)

It is evident that Eq. (5) means a recipe how to decompose a linear, convolutive mixture of signals. Hints for the approximation of condition (6) are explained in detail in [1], and will also be taken into account in the sequential form of CKC derived in the following section.

Sequential CKC builds on the batch CKC by changing the correlation matrix computation from segments to iterative sample-by-sample approximation, and by constructing an improved version of the premultiplying vector y T (n0 ) in Eq. (5) in the same iterative manner. Subsections III.A and III.B reveal these processes in more detail, respectively. A. Sequential inverse of correlation matrix To create a sequential CKC the biggest problem is posed by the pseudoinverse of correlation matrix C #yy in (5). The most obvious way for the correlation matrix calculation is based on longer signals segments. The matrix is a square matrix and owing to additive noise as defined in Eq. (2), which is always present in real situations, correlation matrix has a full column rank. This means that instead of pseudo−1

can, in generally, be inverse , direct matrix inverse implemented. With such a starting point, a few different methods for the sequential matrix inverse calculation are available. It was shown in [2] that the fastest one is the so called Sherman-Morrison formula published in [3] as: ( A + uv T ) −1 = A −1 −

A − 1 uv T A − 1 1 + v T A −1 u

Now, assume that correlation matrix is known after the k-th observation sample. When the next sample vector is observed, correlation matrix in step k+1 calculates as follows: C y y , k +1 = C y y , k + y ( k + 1) y ( k + 1) T ,

(9)

where C yy ,k is correlation matrix after the k-th sample

III. SEQUENTIAL CONVOLUTION KERNEL COMPENSATION

#

Fig. 1: Estimated pulse sequence obtained by sequential CKC

(8)

where A is a square matrix, u and v are column vectors.

vector of observations. It is clear that the right-hand side of Eq. (9) corresponds to the left-hand side of (8), which also suggests how to compute correlation matrix iteratively. This way causes the correlation matrix coefficients grow by iteration steps and the matrix inverse values decrease correspondingly. The sequentially computed inverse of correlation matrix can be used in Eq. (5), which makes the source pulse trains v n0 (n) computable sequentially as well. The pulse amplitudes decrease in time because of decreasing inverse matrix values, as shown in Fig. 1. To avoid the problem of decreasing values, correlation matrix is averaged across the number of samples involved, which changes Sherman-Morrison formula to: ⎛ k A + uv ⎜ ⎜ k +1 ⎝

T

⎞ ⎟ ⎟ ⎠

−1

⎛ A −1 A −1 ⎜ −1 uv T A k = (k + 1)⎜ − k −1 ⎜ k T A ⎜ 1+ v u k ⎝

⎞ ⎟ ⎟, ⎟ ⎟ ⎠

(10)

where k is the iteration step number. B. Improvement of pulse trains Premultiplying vector y (n0 ) in Eq. (5) constitutes a linear filter, together with the inverse of correlation matrix. The quality of decomposed trains depends on the correctness of this sample vector, i.e. if condition (6) is fulfilled. Now assume that k different sources trigger at the same time

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An Approach to the Real-Time Surface Electromyogram Decomposition

vn0 n1 (n) = ν n0 (n) ⋅ν n1 (n)

(11)

generates a superimposition of only those pulse trains that trigger in both positions n0 and n1. This principle can be extended to additional “filtering” positions. It was estimated in [1] that already four proper positions combine in a pulse train which most probably belongs to a single source, e.g. the j-th one. Denoting this train by v n0 n1n2 n3 (n) , suppose the pulses in this train appear at t0, … , tg-1. Then by averaging all the observation vectors in these instants, an optimum filter is constructed as follows: fj =

1 g −1 ∑ y (ti ) g i =0

(12)

where fj stands for the j-th source filter. It is updated throughout the sequential decomposition process, and so is the verification (11). The sequential decomposition begins by selecting the initial filter positions n0 for a certain number of foreseen trains. These initial positions are decided according to the activity index values. Afterwards, the filters fj are updated according to (11) and (12) for all the estimated pulse trains in parallel. The proposed sequential decomposition algorithm is depicted in Fig. 2. 0:

Initialization: Calculate starting matrix inverse (SVD) and select fj

1:

A new observation vector is obtained, y(k+1).

2:

Using (10) the new inverse of correlation matrix is calculated.

3:

Calculate new pulse train samples: ν n 0 ( k + 1 ) = f Tj C #y y y ( k + 1 )

for

all

foreseen

sources j; if any of these samples exceeds a preselected threshold, go to 4, otherwise go back to step 1. 4:

Update fj according to (12), checking first (11); go back to 1.

Fig. 2: Pseudocode of the sequential CKC decomposition approach.

IV. SIMULATION RESULTS The sequential CKC was tested in two experiments. The first experiment evaluated the influence of noise, while the second experiment studied the influence of different number of observed sources. The decomposed pulse trains were assessed with three different statistics: the rate of properly placed pulses, the rate of missed pulses, and the rate of misplaced pulses. At the same time, the number of properly decomposed sources was also counted. A pulse train was considered properly decomposed if more than 95% of its pulses were placed correctly. A. Influence of noise First experiment was conducted in 20 Monte-Carlo runs of sequential CKC per each signal-to-noise ratio (SNR). Three different ratios were tested: 20 dB, 15 dB and 10 dB. In each run 10 simulated signals (SEMG) which were 10000 samples long with maximum voluntary contraction (MVC) of 10% were used. The CKC extension factor K was set equal to 5. The initial observation window size for the initial correlation matrix was selected at 250 samples. Number of filters has been limited to 10. A sample in the reconstructed train was considered a triggering pulse if it exceeded 80% of the average amplitude of three highest pulses in every subsequent analysing window. Fig. 3 shows that noise does not affect the accuracy of estimated pulse trains. The recognition rate depends more on the selection of good starting filters than on small SNR. Although the average rate is about 75% of properly placed pulses, we still detect more than a half of trains with 95% correctly placed pulses as shown in Fig. 4. This fact is also confirmed by Fig. 6. As we can see in Fig. 5, there were about 25% of misplaced pulses and no missed pulses, which is indicating that wrong pulses belong to the supreimpositions of several trains. Rate of properly placed pulses [%]

instant n0. The filter placed at n0 extracts a superimposition of all k pulse trains. Even worse, these pulse trains are corrupted by noise in practice. To improve the decomposition results, filter updating is necessary along with the progress of the sequential decomposition. Assume n0 and n1 are two different time moments, both satisfying condition (6) for the same source. The element-wise product

107

90 70 50 20

15

10

Signal-to-noise ratio [dB] Fig. 3: Rate of properly placed pulses versus different SNRs

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the number of sources with 10% MVC and 30% MVC. The SNR was set equal to 10 dB in all test runs. More sources cause bigger probability of the superimpositions of different source triggerings. This fact causes a decrease in the number of properly recognized pulse trains. Fig. 6 depicts the number of different recognized pulse trains versus the different MVC levels, i.e. the number of source, where the maximum number of sources followed in parallel by our method was limited to 10. V. CONCLUSION

Rate of misplaced and missed pulses [%]

Fig. 4: Triggering pulse train (light) and estimated pulse train (dark)

40 20 0 20

15

10

Signal-to-noise ratio [dB] Misplaced

Missed

We derived an iterative, sequential version of CKC. It mimics the steps of the batch CKC, but all the elements are calculated in a sequential manner being updated with every new input sample vector. The correlation matrix inversion, which is the most computationally complex, was implemented by the Sherman-Morrison formula. It proves to be O (( KM ) 2 ) and also almost five times faster than the updating of 10 filters. Extensive statistical testing with synthetic SEMG signals proved that all the advantages and performance of the CKC method were also kept by its sequential version.

Number of different recognized pulse trains

Fig. 5: Rate of misplaced and missed pulses versus different SNRs

6

REFERENCES 1.

5 4 3

2.

2 1 0 10

30

3.

MVC

Fig. 6: Number of different recognized pulse trains versus different pulse triggerings.

B. Influence of different number of sources The second experiment was also conducted with 20 runs of sequential CKC per each number of sources. The simulated signal sets were generated in the same way as with the first type of experiments. The only difference was related to

4.

A. Holobar, D. Zazula: Multichannel Blind Source Separation Using Convolution Kernel Compensation, IEEE Trans. on Sig,. Proc., in press. V. Glaser, Sequential Convolution Kernel Composition for Composite Signal Decomposition, Diploma Thesis, FERI, University of Maribor, 2006. C. D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, Philadelphia, 2001. A. Holobar: Blind decomposition of convolutive mixtures of close-to-orthogonal pulse sources applied to surface electromyogram, PhD Thesiss, FERI, University of Maribor, 2004. Author: Institute: Street: Country: Email:

Vojko Glaser Faculty of EE and CS Smetanova 17, 2000 Maribor Slovenia [email protected]

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EMG Based Muscle Force Estimation using Motor Unit Twitch Model and Convolution Kernel Compensation R. Istenic1, A. Holobar1,2 , R. Merletti2 and D. Zazula1 1

Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia 2 LISiN, Department of Electronics, Politecnico di Torino, Torino, Italy

Abstract— In this paper we introduce a new method for muscle force estimation from multi-channel surface electromyograms. The method combines a motor unit twitch model with motor unit innervation pulse trains, which are estimated from multi-channel surface electromyograms. The motor unit twitches are then aligned to the innervation pulse trains and summed up to obtain the total muscle force. The method was tested on real surface EMG signals acquired during force ramp contractions of abductor pollicis brevis muscle in 8 male subjects. With 22 ± 5 (mean ± std. dev.) motor units identified per subject, the force estimation error of our method was 16 ± 4 % RMS. These results were compared to the method which uses the EMG amplitude processing to estimate muscle force. The results of our new concept proved to be completely comparable to those of EMG amplitude processing. Keywords— muscle force estimation, EMG force relation, twitch, convolution kernel compensation.

I. INTRODUCTION Force production in a muscle is regulated by two main mechanisms: the recruitment of motor units and the modulation of their discharge rates. The greater the number of motor units recruited and their discharge frequency, the greater the force will be. The same two mechanisms determine also the electric activity in a muscle. Thus a direct relationship between the electromyogram (EMG) and exerted muscle force might be expected [1]. In order to estimate muscle force from surface electromyograms (SEMG), we have to know the relationship between the electrical and mechanical behavior of muscles. When the electric response of muscles is measured by SEMG, only part of the muscle and active motor units (MU) is detected. Another problem that arises is that superficial MUs contribute more to the observed SEMG than deep MUs. As we know that the force produced by a muscle means a resultant of all MU forces, we also have to take into account that the MUs trigger with different frequencies, they may be recruited at different time intervals, and the amount of force they exert at every excitation (the force twitch) depends on the MU type [2]. Reference muscle forces are measured externally via the moments they cause in the observed joint or extremities.

This means that we hardly measure the force of just one muscle. Usually, several muscles contribute to the force level which we detect. Knowing all these facts, we are trying to verify and validate two approaches that predict the muscle forces from the SEMG observations. We decided to use isometric ramp contractions, because this kind of exercise gives higher probability that we really observe the force level produced just by one selected muscle. By constant force contractions, it can happen that the tested subject produces force by activating also other muscles, which are not under the recording electrodes. In the sequel, we reveal our new force estimation approach which sums up the MU twitches aligned by the decomposed innervation pulse trains. We briefly summarize the muscle force generation model introduced by [2] and two methods for muscle force estimation: the SEMG amplitude processing and the convolution kernel compensation (CKC) decomposition. Section III explains the experiments and compares the model-based forces obtained on real SEMGs with the real measured forces. Sections IV and V discuss the results and conclude the paper. II. METHODS A. Muscle force model We adopted the muscle force model proposed in [2]. The complete pool consisted of 120 motor units. The distribution of twitch forces for the motor units was represented as an exponential function [2]. A large number of motor units produced small forces, while relatively few units generated large forces. Twitch force was modeled as the impulse response of a critically damped, second order system. Fuglevand [2] used Eq. 1 to represent a motor unit twitch:

f (t ) =

P ⋅ t 1−( t / T ) , ⋅e T

(1)

where T is contraction time to peak force of the twitch and P is its peak amplitude. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times

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EMG Based Muscle Force Estimation using Motor Unit Twitch Model and Convolution Kernel Compensation

B. Muscle force estimation using SEMG amplitude processing The majority of today’s methods use SEMG amplitude to estimate force. They rely on conventional SEMG amplitude processing, such as rectification followed by low pass filtering, to preprocess the SEMG before relating it to torque. Clancy et al. [3] found that advanced SEMG processing that incorporates signal whitening and multiple-channel combination significantly improve force estimation. Both whitening and multiple-channel combination reduced EMG-torque errors and their combination provided an additive benefit. Potvin et. al [4] found that high pass filtering of SEMG signals improves SEMG based muscle force estimates. An iterative approach was used to process the EMG from the biceps brachii, using progressively greater high pass cutoff frequencies (20-440 Hz in steps of 30 Hz) with first and sixth order filters, to determine the effects on the accuracy of force estimates. The results indicate that removing up to 99% of the raw SEMG signal power resulted in significant and substantial improvements in force estimates. For the purpose of force prediction, it appears that a small high band of SEMG frequencies may be associated with force while the remainder of the spectrum has little relevance. C. Our method For a muscle force modeling, described in Subsection II.A, innervation pulse trains of individual MUs are needed. They were obtained from recorded SEMG signals with the Convolution Kernel Compensation (CKC) method described in [5] and [6]. The method extracts MU discharge patterns in lowlevel (0-10% MVC) force-varying isometric contractions, is not sensitive to superimpositions of action potentials and detects on average a larger number of MUs that it is usually possible with single-channel intramuscular EMG [5]. However, it is able to recognize only superficial MUs, which have the greatest impact on SEMG. As a result, we deal with a limited MU pool. In our experiment, the method recognized 22 ± 5 MUs per subject (mean ± std. dev.). This number is substantially smaller than the one proposed in [2] (120 MUs),

Motor unit twitch forces → MU22

1.4

Twitch force (arb. units)

were inversely related to twitch amplitudes [2]. The range of twitch forces used in the model was 100-fold. One unit of force was equivalent to the twitch force of the first unit recruited, and the last unit recruited had a twitch force of 100 units. The range of twitch contraction times was 3-fold, with the twitch for the first recruited unit having the time to peak duration of 90 ms, and for the last recruited unit of 30 ms. All MUs followed the widely reported sigmoidal relationship between MU force and firing rate. The total force of the muscle was determined as a linear summation of all the individual MU forces.

115

1.2 → MU1

1 0.8 0.6 0.4 0.2 100

200

300

400 500 Time (ms)

600

700

800

900

Fig. 1 MU twitch forces assigned to recognized MUs for contraction up to 10 % MVC. Only low threshold motor units were assumed to be recruited with twitch forces from 1 to 1.6 units and contraction times from 90 ms to 80 ms.

hence, the twitch force and twitch contraction time ranges from [2] had to be modified. Firstly, with recorded contractions ranging from 0 to 10 % maximum voluntary contraction (MVC), we assumed only low-threshold units are recruited. To correlate MUs with twitch forces correctly, the recognized MU innervation pulse trains were sorted according to the recruitment order. The first recruited MU was assigned twitch force of 1 unit with contraction time of 90 ms, while the last recruited MU had a twitch force of 1.6 units with contraction time of 80 ms. Such values would be assigned to the first twenty units of all 120 MUs in model [2] (see Fig. 1). Model [2] allows also gain in MU twitch force to vary as a function of the firing rate. The maximum gain is obtained when contraction time of the twitch equals the interstimulus interval (interval between two firings). This gain factor was used to amplify the MU twitch force for each discharge. The force produced by a single MU is equivalent to the sum of individual amplified twitches. The mechanical actions of MUs were assumed to be independent of one another, thus the total force in the muscle was determined as the sum of the individual MU forces. Both measured and estimated forces were filtered with first order Butterworth low pass filter with cutoff of 1Hz. III. EXPERIMENTAL RESULTS A. Data acquisition SEMG recording took part at LISiN, Department of Electronics, Politecnico di Torino. Eight healthy male subjects (age 27.0 ± 2.3 years, height 181.1 ± 6.7 cm and weight of 75.5 ± 9.0 kg) participated to the experiment. SEMG signals

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9 8 7 6 5 4 3 2 1 0 0

10

20

30

40 50 Time (s)

60

70

80

90

Fig. 2 Comparison of measured force (thick solid line), force estimated with our method (dashed line) and force estimated with Potvin's method (thin solid line). The forces were estimated from real SEMG signals (subject A) Comparison of Measured and Predicted Force 9

B. Results

RMS ( f estimated − f measured ) = ⋅ 100 (2) RMS ( f measured )

Results of our method were compared to the Potvin’s method described in [4]. The method builds on a single SEMG channel and uses high-pass filtering to enhance the force estimation. The high pass cutoff frequency was set equal to 180 Hz, the low pass cutoff frequency to 1 Hz while the non-linear normalization constant was 23 [4]. With 60 SEMG channels available, only the channel with the smallest RMS error was taken into consideration. The measured and predicted muscle forces for subject A (7 consecutive ramp contractions) are depicted in Fig. 2. Our method yields 9.9% error (Eq. 2) and correlation coefficient of 0.98, while Potvin's method 12.2% error and correlation coefficient of 0.96. Fig. 3 depicts the results for the 6th force ramp only.

8 F orce Am plitude (% MVC)

To compare estimated and measured force and to calculate estimation error, both forces must be normalized first. In our case, linear normalization w.r.t. the maximum force amplitude was used. Estimation error was computed as root mean square (RMS) percent error (Eq. 2). In addition, the correlation coefficient between signals was also computed.

ErrorRMS %

Comparison of Measured and Predicted Force

10

F orce Am plitude (% MVC)

were acquired by a matrix of 61 electrodes arranged in 5 columns and 13 lines (with four corner electrodes missing). Inter-electrode distance was 3.5 mm. The electrode matrix was located with the columns in the direction of the muscle fibres and covered the entire distal semifibre length (from the innervation zone to the distal tendon) and part of the proximal semifibre of abductor pollicis brevis muscle. Before electrode placement, the skin was abraded with abrasive paste. The matrix was fixed on the skin by adhesive tape and a reference electrode was placed at the wrist. A custom designed brace was used to measure abduction force. The subject’s wrist was fixed in a padded wood support with the head of the thumb phalanx in touch with a load cell. The force signal was amplified, provided as feedback to the subject on an oscilloscope, and recorded in parallel with the SEMG signals. The subjects performed three maximal voluntary contractions separated by 2-min rest, after which the electrode grid was located over the abductor pollicis brevis. The subject was then asked to linearly increase force from 0% to 10% MVC in 6 s and then decrease from 10% to 0% MVC in other 6 s, using the visual feedback on force. All together, 7 consecutive force ramps were recorded from each subject. The SEMG signals were amplified, band-pass filtered (3 dB bandwidth, 10-500 Hz) and sampled at 1650 Hz by a 12 bit A/D converter.

7 6 5 4 3 Measured Force Predicted Force - twitch Predicted Force - amplitude

2 1 62

64

66

68 70 Time (s)

72

74

76

Fig. 3 Comparison of measured force (thick solid line), force estimated with our method (dashed line) and force estimated with Potvin's method (thin solid line). The forces were estimated from real SEMG signals (subject A, 6th force ramp) and normalized w.r.t. their peak value. Both methods were tested on signals from all 8 subjects. Errors and correlation coefficients were calculated for each subject. Average error was 15.8% ± 4.2% for our method and 16.1% ± 3.5% for Potvin's method [4]. Average correla-

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EMG Based Muscle Force Estimation using Motor Unit Twitch Model and Convolution Kernel Compensation

tion coefficient was 0.96 ± 0.02 for our method and 0.93 ± 0.03 for the compared one. As found by Potvin [4], high pass filtering of SEMG signals improved the force estimation also by our signals. High pass cutoff frequencies were changed from 20 Hz to 420 Hz in steps of 80 Hz. The optimal sixth order high pass cutoff frequency for our signals was found to be 180 Hz. At that frequency the minimal error was obtained (Table 1).

117

V. CONCLUSION Several simulation studies of the MU force model were proposed in the past [2, 7]. Our study went a step further as we investigated how this model is performing on real SEMG signals. Despite some open issues discussed in Section IV, our method yields comparable results to the Potvin’s method [4] which is based on SEMG amplitude processing.

ACKNOWLEDGEMENT

Table 1 High pass cutoff frequency (Hz)

RMS error

20 100 180 260 340 420

16.8 % 16.3 % 16.1 % 17.3 % 16.7 % 18.4 %

IV. DISCUSSION Our method performed well in comparison to the Potvin’s method. However, there are some open issues to be discussed when interpreting the results of our method. The first one is the number of MUs that can be detected by the pick-up electrodes and recognized by the decomposition algorithm. With surface electrodes only part of all active MUs is detected. Moreover, not all the detected MUs are recognized by the CKC decomposition technique. This implies that our method operates on a limited set of MUs. Question arises whether this set is representative enough for the purpose of force estimation. Typically, the number of MUs contributing to the muscle force is much larger than the number of recognized MUs. Nevertheless, the results of this study demonstrate that, at least in the case of abductor pollicis brevis muscle, recognized motor units form a good base for force estimation. Finally, co-activation of antagonist and agonist muscles must be taken into account. As we stated in introduction, force at a joint is normally produced by several concurrently active muscles. For the best possible force estimations all muscles that contribute to the joint force must be included in SEMG recordings and in the force estimation process.

This work was supported by the Slovenian Ministry of Higher Education, Science and Technology (Contract No. 1000-05-310083 and Programme Funding P2-0041) and European Commission within the Sixth Framework (Project Cybermans) and Marie Curie Intra-European Fellowships Action (DE MUSE, Contract No. 023537).

REFERENCES 1. 2. 3.

4. 5.

6. 7.

Merletti R, Parker P A (2004) Electromyography: Physiology, engineering and noninvasive applications. John Willey & Sons, New Jersey. Fuglevand A J, Winter D A, Patla A E. (1993) Models of recruitment and rate coding organization in motor-unit pools. Journal of Neurophysiology 70:2470-2488 Clancy E A, Bida O, Rancourt D (2007) Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force varying contractions. Journal of Biomechanics 39(14): 2690–2698 Potvin J R, Brown S H M (2004) Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMGbased biceps brachii muscle force estimates. JEK 14:389-399 Holobar A, Zazula D, Gazzoni M, Merletti R, Farina D (2006) Noninvasive analysis of motor unit discharge patterns in isometric forcevarying contractions. ISEK Proc., XVI congress of ISEK, Torino, Italy, 2006, pp 12 Holobar A, Zazula D (2004) Correlation-based decomposition of surface EMG signals at low contraction forces. Med. Biol. Eng. Comput 42:487-495 Zhou P, Rymer W Z (2004) Factors governing the form of the relation between muscle force and the EMG: A simulation study. Journal of Neurophysiology 92:2878-2886 Address of the corresponding author: Author: Rok Istenic Institute: Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor Street: Smetanova 17 City: 2000 Maribor Country: Slovenija Email: [email protected]

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Fast-Slow phase separation of Near InfraRed Spectroscopy to study Oxigenation v/s sEMG Changes Gian Carlo Filligoi1,2 1

2

Dpt. INFOCOM, Faculty of Engineering ,Università degli Studi"La Sapienza", Italy Biomedical Systems Research Center (CISB), Università degli Studi "La Sapienza", Italy

Abstract – The possibility of studying non-invasively local muscle oxidative metabolism during exercise has been recently enhanced thanks to the use of Near-InfraRed Spectroscopy (NIRS). Moreover, the myoelectric manifestations of muscle fatigue occurring during sustained isometric contractions have been extensively studied with quantitative surface electromyography (sEMG) and are described by means of some sEMG global parameters extracted in time- and/or frequency-domain. With the aim of analyzing together both NIRS and sEMG data recorded on Biceps Brachii, an experimental protocol has been applied to seven subjects. During the experimental session, each subject had to perform two different kind of physical trials, a constant force and a cyclically varying isometric contraction. While examining the whole set of data with the aim of investigating the relationship between modifications of sEMG and underlying metabolic status, we faced up with the problem of objectively separating the two main phases of NIRS data. Results clearly indicated the presence of an initial fast phase of muscle desaturation followed by a slow phase, regardless of the kind of exercise. This behavior was paralleled by an analogous rate of change of sEMG parameters, thus suggesting a strong link between the two phenomena. As an objective criterion for separating the fast and the following slow phase an ad-hoc algorithm has been implemented. NIRS & sEMG data have been analysed by considering only those data pertaining rigorously to one of the two phases, whereas data collected during initial, transiction and final phases were discarded. In this paper we present some details on the algorithm for automatic separation of the two phases, with the most important results on statistical significance of the relationship between parameters extracted from sEMG in the fast phase v/s muscle oxigenation. Keywords – Myoelectric signal, sEMG, Near Infrared Spectroscopy, Muscle oxigenation, NIRS Slow-Fast phase.

I. INTRODUCTION Near InfraRed Spectrocopy (NIRS) is a new technique able to offer reliable information on local muscle oxidative metabolism during a physical exercise [1, 2]. The O2 saturation assessment in muscles is possible by using two different NIRS methods [3], and the most widely quoted in the literature, the near infrared spatially resolved

spectroscopy (NIRSRS), was also adopted in our experiments. By means of this technique, we measured several metabolic parameters such as the changes in oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb) concentration, the total hemoglobin volume (simply by adding the two previous parameters, tHb = O2Hb + HHb), and, finally, the average muscle tissue O2 saturation connected to neighbouring blood circulation within small vessels (such as arteriolar & venular, or capillary bed). The latter value provides a good estimation of the dynamic balance between O2 supply and O2 consumption in the investigated muscle area. Concerning the surface ElectroMyoGram (sEMG), the myoelectric signal analysis has been widely applied to investigate early changes occurring in the course of sustained isometric contractions (see [4] for a review). In our approach, time- and frequencydomain typical parameters have been extracted, and in particular Root Mean Square (RMS) and average muscle fiber conduction velocity (CV) in temporal space, whereas median frequency (MDF) in spectral domain. Several studies have combined sEMG data with those regarding muscle oxygenation (by NIRS) during exercise [5-10] and it is generally accepted that a linear relationship connects level of isometric effort with sEMG amplitude and muscle oxygen uptake. Though those studies demonstrates feasibility and importance of coupled sEMG-NIRS measurements, the heterogeneity in the tested muscles and exercise protocols, a somehow limited sEMG analysis techniques adopted as well as the intrinsic methodological limitations of the first generation NIRS devices utilized, suggest further investigations. On the basis of the above considerations, the present study was designed to investigate the relationship between myoelectric and metabolic (oxidative) changes occurring in the biceps brachii muscle (BB) of a group of seven healthy subjects (age: 34±6 years, body mass: 74±6 kg, height: 173±6 cm) during sub-maximal isometric contractions. The sEMG and NIRSRS measurements were performed during 30-sec. submaximal constant isometric force (at an effort equal to 20, 40, 60, and 80% MVC-the Maximal Voluntary Contraction,

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Fig.1 Method used to determine the linear portion of the fast and slow phase of ΔSRS-O2. The plot refers to an ST at 80% MVC contraction. measured before of the experiment - called ST20, ST40, ST60 and ST80, respectively, being ST the acronym for STeady State contraction) and cyclically varying force isometric contractions (40±20%MVC; 60±20%MVC, called SIN40 and SIN 60, respectively, with SIN used as a contract form for SINusoidally varying isometric contraction). We hypothesize that different isometric constant force levels, as well as isometric cyclically varying muscle activation might have a different impact on local blood flow, and this might be reflected by different myoelectric and metabolic responses. Some deeper reasons for the adoption of the present experimental protocol comprising two different isometric exercises are presented elsewhere [11] as well as the details of the experimental setup and the instrumentation.

II. FAST AND SLOW PHASE AUTOMATIC SEPARATION While approaching the problem of looking for the relation among the various parameters extracted from both sEMG and NIRS recordings, we observed that data pertaining to the initial fast phase of muscle desaturation had to be objectively divided from those collected during the following slow phase, regardless of the kind of exercise. In fact, if we observe the average tissue O2 saturation index measured by means of the spatially resolved spectroscopy (SRS) technique (and, in particular, the index value ΔSRSO2 given by the percentage of O2 saturation with respect to its value at rest, evaluated just before the experiment [11]), the relative plot shows two evidently distinct phases: after

an initial brisk decrease (fast phase), ΔSRS-O2 presented an evident lower decay rate (slow phase). In Fig.1, it is shown an example of NIRS data collected during ST80, i.e., a constant force isometric contraction at 80%MVC. The problem to be solved was to find an objective procedure able to separate the two different behaviours. After several different attempts, it has been decided to adopt the following strategy for the selection of the two fiducial points representing the initial and the end of the fast phase: 1)- NIRS-data were first smoothed by a 7-degree polynomial interpolation procedure; 2)- the following step of the relative algorithm was to look for a very low oscillating negative long plateau in the first derivative of the relative smoothed ΔSRS-O2 time-course. Obviously, this plateau was preceeded and followed by an easily detectable decreasing and increasing phase; 3)- The fast ΔSRS-O2 decreasing phase was quantified by the slope of least square regression line; 4)-The exact definition of the two fiducial points marked as initial and final points was taken by finely choosing a suitable threshold which had to be tuned on the basis of the distance of those points from the regression line. The whole set of parameters extracted either from NIRS data or from sEMG recordings were evaluated uniquely within the fast–phase interval (indicated in Fig.1 as Regression Interval) together with the statistical significance of their eventual relationship (see RESULTS section). This simply because the slow-phase showed a markedly constant ΔSRS-O2 behaviour in all subjects and in all experiments consisting in a very low value of negative slope of the regression line (ranging from -0.0013%, which

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corresponds practically to a constant variation with time, and –1.2%, that represents, only for one case and in a unique subject, the maximal rate of decrease of ΔSRS-O2 during the slow phase). All various values of the parameters extracted from sEMG and NIRS during slow-phase and evaluated one versus the others did not show any statistically significant difference.

slow-phase gave satisfactory results. The test for statistically significative differences gave the results shown in Table 1. The relative time-course behaviour of the parameters extracted from sEMG and NIRS are reported elsewhere [11] and are far from the scope of this paper mainly devoted to the presentation of our technique, able to separate the two phases of muscle tissue oxigenation.

III. RESULTS As a main result, we may say that in all cases during the slow-phase, MNF slope, CV slope and relative NIRS parameters did not show any statistical difference when evaluated one against the others among various ST and SIN isometric contractions. At the contrary, while the automatic procedure for fast phase separation worked fairly well in all cases of ST contraction (see Fig.2), some more difficulties arised in SIN contraction due to the intrinsic oscillations of the relative parameters extracted during the cyclically varying exercises (see Fig.3). Nevertheless, the application of the previously discribed procedure for automatic definition of onset and offset ΔSRS-O2 fiducial points to

Fig.2 ΔSRS-O2 during ST exercises at the different force levels

Table 1: Each table column reports results of the statistical comparison performed (two-tailed t-Test for paired data; p<0.05) between the variables indicated in the upper part of the column during the two different physical tasks indicated in the left side. The X symbol denotes the presence of statistical significance (after Bonferroni correction when appropriate). COMPARISON

ΔSRS-O2 min

ΔSRS-O2 slope (fast phase)

ST20 – ST40

X

X

X

X

ST20 - ST60

X

X

X

X

ST20 - ST80

X

X

X

X

X

X

X

ST60 - ST80

X

X

X

SIN40 - SIN60

X

X

ST40 - ST60 ST40 - ST80

SIN40 - ST20

MDF slope (fast phase)

CV slope (fast phase)

X X

X

X

X

SIN40 – ST40

X

X

SIN40 – ST60

X

X

X

X

SIN60 – ST40

X

X

SIN60 – ST60 SIN60 – ST80

X

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3.

Fig.3 ΔSRS-O2 during SIN exercises at 40+/- 20 % and 60 +/-20% force levels

IV. DISCUSSION In substance, the results of the present paper can be resumed as follows: • the initial rate of BB desaturation is related to the level of effort, and is not dependent upon the kind of isometric exercise; • the same robust relationship exists between force and rate of change of myoelectric parameters. It thus emerges that the rate of change of sEMG parameters is strongly linked to the initial rate of muscle desaturation. Moreover, during the low phase for both ST and SIN contractions, even at the highest force levels, tHb data suggest that muscle blood flow is not completely restricted. Concerning the fast phase, Biceps Brachii desaturation (ΔSRS-O2) at the onset of isometric exercise shows an evident delay.

REFERENCES 1.

Boushel R, Langberg H, Green S, Skovgaard D, Bulow J, and. Kjaer M (2000) Blood flow and oxygenation in peritendinous tissue and calf muscle during dynamic exercise in humans. J. Physiol. Apr 1;524 Pt 1:305-13.

2.

Quaresima V, Komiyama T, and Ferrari M (2002) Differences in oxygen re-saturation of thigh and calf muscles after two treadmill stress tests. Comp. Biochem. Physiol. 132:67-73.

Delpy DT and Cope M (1997) Quantification in tissue near-infrared spectroscopy. Philos. Trans. R. So.c Lond. B. Biol. Sci. 352:649-659. 4. De Luca C.J (1997) The use of electromyography in biomechanics. J. Appl. Biomech. 13:135-163. 5. Burnley M, Doust J.H, Ball D, and Jones A.M (2002) Effects of prior exercise on VO2 kinetics during heavy exercise are related to changes in muscle activity. J. Appl. Physiol. 93:167-174. 6. Kouzaki M, Shinohara M, Masani K, Tachi M, Kanehisa H, Fukunaga T (2003) Local blood circulation among knee extensor synergists in relation to alternate muscle activity during low-level sustained contraction. J. Appl. Physiol. 95:49-56. 7. Miura H, Araki H, Matoba H, and K. Kitagawa K (2000) Relationship among oxygenation, myoelectric activity, and lactic acid accumulation in vastus lateralis muscle during exercise with constant work rate. Int. J. Sports Med. 21:180-184. 8. Praagman M, Veeger H.E.J, Chadwick E.K.J, Colier W.N.J.M, and Van Der Helm F.C.T (2003) Muscle oxygen consumption, determined by NIRS, in relation to external force and EMG. J. Biomech. 36:905-912. 9. Takaishi T, Sugiura T, Katayama K, Sato Y, Shima N, Yamamoto T, and Moritani T (2002) Changes in blood volume and oxygenation level in a working muscle during a crank cycle. Med Sci Sports Exerc 33: 520-528. 10. Yoshitake Y, Ue H, Miyazaki M, and Moritani T (2001) Assessment of lower-back muscle fatigue using electromyography, mechanomyography, and nearinfrared spectroscopy. Eur. J. Appl. Physiol. 84:174-179. 11. Felici F, Quaresima V, Fattorini L, SbricColi P, Filligoi G, and Ferrari M (2007) (in course of publication) Biceps brachii myoelectric and oxygenation changes during static and cyclic isometric exercises. J. Electromyogr. and Kinesiol.

Author: Institute: Street: City: Country: Email:

Gian Carlo Filligoi Dpt. INFOCOM, Faculty of Engineering, Universita degli Studi "La Sapienza" Via Eudossiana 18 00185 ROMA Italy [email protected]

1.

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Model Based Decomposition of Muaps Into Their Constituent Sfeaps M.G. Xyda1,2, C.S. Pattichis1,2, P. Kaplanis1,2, C. Christodoulou1,2 and D. Zazula3 1

Department of Computer Science, University of Cyprus, Cyprus The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus 3 Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia 2

Abstract— The motor unit action potential (MUAP) represents the spatial and temporal summation of single fibre extracellular action potentials (SFEAPS) generated from the same motor unit. In this study, a model based decomposition of MUAPS into their constituent SFEAPS is investigated. The aim of this study has been to develop a system that will give to the neurophysiologist a visualization of an “estimated” structural organisation of the motor unit, which includes information about number of fibres, fibre distribution and positioning and fibre diameter. The mathematical model developed in two dimensions by Dimitrova and Dimitrov [1], [2] was used to generate SFEAPS. In addition, this model was extended to three dimensions. Two-dimensional and three-dimensional models of MUAPS consisting of 1 to 10, and 50 fibres were developed and decomposed for a small recoding radius up to 5 mm. The non linear least squares optimization procedure based on the Levenberg-Marquardt algorithm was used to obtain a solution to the MUAP decomposition problem i.e. fibre distribution, positioning and diameter. Using this method, a satisfactory solution to the decomposition problem was obtained. Future work will investigate the usefulness of the proposed analysis on MUAPS recorded from normal subjects and subjects suffering with neuromuscular disorders. Keywords— EMG, MUAPs, SFEAPs, Modeling, Decomposition.

a model based decomposition of MUAPS into their constituent SFEAPs is investigated. Similar work to this study was carried out for decomposing surface EMG into SFEAPs based on neural networks [3]. In addition, several studies investigated successfully, the decomposition of needle EMG into its constituent MUAPs [4]-[6]. Moreover, a decomposition of the Compound Action Potential in EMG was proposed [7]. The aim of this study was to develop a system that will give to the neurophysiologist a visualization of an “estimated” structural organisation of the motor unit, which includes information about number of fibres, fibre distribution and positioning and fibre diameter. This consists of the inverse problem in EMG. II. METHODOLOGY A. Simulation of SFEAPs The model developed by Dimitrova and Dimitrov [1], [2] was used in order to generate SFEAPS where the muscle fibre is considered as a dipole. The model has been implemented as a convolution within a specific time interval as follows: 1 ⎞ ⎛ ϑ( ) ⎟ ⎜ ϑ φ ran ⎟ Φ( x0k , y0k ,dk ,t)= - Ce ⎜ i * ⎜ ϑx ϑx ⎟ ⎜ ⎟ ⎝ ⎠

I. INTRODUCTION Electromyography (EMG) studies the electrical activity of the muscle, and forms a valuable aid in the diagnosis of neuromuscular disorders. EMG findings are used to detect and describe different disease processes affecting the motor unit, the smallest functional unit of the muscle. During slight voluntary contraction individual units are recorded, known as Motor Unit Action Potentials (MUAPs). The motor unit action potential (MUAP) represents the spatial and temporal summation of single fibre extracellular action potentials (SFEAPs) belonging to the same motor unit. MUAP morphology is affected by structural reorganisation of the motor unit that takes place due to disorders affecting peripheral nerve and muscle. MUAP features extracted in the time domain like duration, amplitude and phases are extensively used by the neurophysiologist for the assessment of neuromuscular disorders. In this study,

(1)

φ i is the intracellular action potential = A1 x A2 e xA3 and A1 = 72234 , A2 = 5 , A3 = -11 are constants. x = ut u = 4 m/s is the propagation velocity which is considered constant. L1 , L2 are the distances between the endplate and the two

edges of the fibre Ce =

d 2 K an V m σ i where σ an = σ x σ y 16 σ an

d is a fibre diameter σ ι = 1.01 is the conductivity inside the fibre and

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Model Based Decomposition of Muaps Into Their Constituent Sfeaps

σ x = 0.33 σ y = 0.63

is the conductivity along the x axis is the conductivity along the y axis

K an = 5

is the medium anisotropicity

x0 , y0 are the horizontal and vertical distances of the electrode from the endplate and considered 0. Vm = 1 is the intracellular action potential amplitude which is considered unity 2

2 r an = (x - x0 ) + K an (y - y 0 ) is a distance between the

electrode ( x0 , y0 ) and fibre endplate, considering the medium anisotropicity.

B. Simulation of MUAPs The structural organisation of normal motor units in human biceps brachii muscle was used as the basis of the simulations. A similar structure was also used by Nandedkar and co-workers [8]. Motor units were simulated by randomly distributing 50 muscle fibres within a circular territory of 5.64 mm diameter. The mean and standard deviation of the diameter of muscle fibres in the motor unit were 50 μm and 28 μm, respectively [9]. The endplates were assumed to be distributed within a 5 mm wide zone midway along the length of the muscle fibres [10]. It was assumed that the recording electrode was positioned at the centre of the motor unit circular territory, at the centre of the endplate zone. The MUAP was computed by summing the SFEAPs within a predetermined semicircular recordingtesting area facing the electrode as follows: Φ(ti ) =

∑ K =1 Φ(xk , yk , d k , tik ) i = 1,… N , M

(2)

where M is the number of fibres positioned within the testing area, and N is the maximum number of sample points. In the first case, a MUAP has been simulated as a sum of SFEAPS generated for 1, 4, 5 and 10 fibres enclosed within a semicircular area of radii 0.6, 0.9, 1.0 and 1.2 mm. The experiments start with 1 fibre in order to determine the behaviour of a single fibre before extending the model. In the second case, a MUAP has been simulated as a sum of SFEAPS generated for 50 fibres enclosed within a semicircular area of radii 5 mm. In this case, a third dimension has been added and also the model has been applied for myopathic cases.

C. Decomposition The decomposition of simulated MUAPS into their constituent SFEAPs was addressed as an unconstrained optimi-

119

zation problem. The non-linear least squares optimization procedure based on the Levenberg-Marquardt algorithm [11] was used to obtain a solution to the MUAP decomposition problem, i.e. fibre distribution and positioning, and fibre diameter. The problem to be solved was formulated as follows: minimize pε R n

{F(p) = ∑

N i=1

(R(p,t i ) - Φ( t i ))2}

(3)

where p[x01 , y01 , d1 ,.....x0 M , y0 M , d M ] is the n-dimensional unknown vector, R is a reconstructed MUAP defined as follows: R ( p , t ) = −C e ∫

L1

− L2

ϑϕ1 ϑχ

1 ) ran dx ϑx

ϑ(

(4)

The equation (3) also gives the error function. The n-dimensional vector has dimensions lM, where l is three, representing variables x01k , y0 k , d k of the SFEAP.

D. Simulation and decomposition for a 2-D model The simulation and decomposition for the 2-D models was carried out as follows:

Step 1. Generate the random numbers to create the components of each fibre for the x -horizontal distance, y vertical distance and d -diameter. The same generator was used to produce values for 1, 4, 5 and 10 fibres in a test radius of 0.6 mm, 0.9 mm, 1 mm and 1.2 mm respectively. The generator was used to produce simulated and initial values. Step 2. Develop the model using equation (1) to simulate a SFEAP. Step 3. Sum the SFEAPS using equation (2) to produce the MUAP in all cases. Step 4. Decompose the MUAP into its constituent SFEAPS using equation (3). The non-linear least-squares optimization procedure based on the Levenberg-Marquardt algorithm was used (matlab function leastsq( )). Compare simulated and the forecasted signals. The forecasted signals were developed by applying the above model using initial values.

E. Simulation and decomposition for a 3-D model The simulation and decomposition for the 3-D model was carried out as follows:

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Step 1. Generate random numbers to create the components of each fibre for x -horizontal distance, y -vertical distance, z − z distance, d -diameter and t-delay. The generator was used to produce simulated and initial values for 50 fibers in a test radius of 5mm.

0.8

0.7

0.6

0.5 y mm

Step 2. Apply equation (1) to develop the model adding a third dimension z. Therefore the distance between the electrode ( x0 , y0 , z0 ) and the fibre endplate, the third dimension

0.9

0.4

0.3

z is r an = ( x - x0) 2 + K an ( y - y 0) 2 + K an ( z - z 0) 2 .

0.2

0.1

Step 3. Same as for the 2-D case. Step 4. Same as for the 2-D case.

0 -0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

x mm

Simulated . Forecasted -

0.6

F. Simulation and decomposition for a 3-Dmodel for myopathy

0.4

Three different cases of myopathy were examined:

0.2

•

III. RESULTS MUAP simulations with different number of fibres using the 2-D and 3-D models were carried out. For each experiment, the simulated values, initial values and final results (forecasted values) are given for the x -horizontal distance, y -vertical distance and d -diameter. Plots of the position of the coordinates of the simulated and final values, as well as the simulated and final waveforms are given.

A. MUAP simulation for a 2-D model with 10 fibres Figure 1 illustrates the simulation and decomposition for a 2-D model with 10 fibres for a test radius of 1.2 mm. The solution was achieved within 97 iterations with an infinitely small error. Figure 1 illustrates clearly the overlap of the coordinates between the simulated and final values as well as the overlap between the simulated and final waveforms.

0 EAP (m v)

Variability in diameter, where the mean diameter of the fibres changes. • Loss of half of the fibres • Reinnervation of fibres. Twenty five fibres were added: a. In the whole MU territory and b. In the right plane of the MU territory. Extending the model to three dimensions and adding more fibres increases the computational complexity and performance of the algorithm.

-0.2

-0.4

-0.6

-0.8

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Time(ms)

Fig. 1 Simulation and decomposition of 10 fibres for a test radius of 1.2 mm (simulated blue ‘.’, forecasted red ‘-‘). The top graph shows the positioning of the fibres (simulated blue ‘o’, forecasted red ‘+’).

B. MUAP simulation for a 3-D model with 50 fibers MUAP simulations with 50 fibres using a 3-D model were carried out. Plots of the simulated and final waveforms are given in Fig. 2. The model converged within 5065 iteration with an infinitely small error (2.7x10-8). Figure 2 illustrates the closeness of the coordinates between the simulated and the final values as well as the overlap between the simulated and the final waveform.

C. MUAP simulation for 3-D, loss of half of the fibres A MUAP simulation and decomposition for a 3-D model with a loss of half of the fibres (from 50 to 25) for a test radius of 5 mm representing the case of a corresponding MUAP recorded in myopathy was carried out. A solution was obtained within 2500 iterations, with an almost infinitely small error (3.8x10-7)

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3 3

2

2 1

0 z mm

z mm

1

-1

0 -1

-2

-2

-3 4 2 0 -2 -4

y mm

-4

0

-2

-3 3

4

2

2

3

1

2

0

1 0

-1

-1

-2

x mm

-3

y mm

-2 -3

Simulated . Forecasted -

x mm

1.4 Simulated . Forecasted -

1.2

0.3

1 0.2

0.6 0.1

0.4

EAP(mv)

EAP(mv)

0.8

0.2 0

-0.1

-0.2 -0.4 -0.6 0

0

-0.2

0.5

1 Time(ms)

1.5

2

Fig. 2 Simulation and decomposition of 50 fibres for a test radius of 5 mm. No of iterations=5065 (simulated blue ‘.’, forecasted red ‘-‘). The top graph shows the positioning of the fibres (simulated blue ‘o’, forecasted red ‘+’).

Figure 3 illustrates the closeness of the coordinates between the simulated and final values as well as the overlap between the simulated and final waveform.

D. MUAP simulation for a 3-D model, reinnervation of 25 fibres in the right plane of the MU territory A MUAP simulation and decomposition for a 3-D model with a reinnervation of 25 fibres (from 50 to 75) for a test radius of 5 mm representing the case of a MUAP recorded in a corresponding case in myopathy was carried out. A solution was obtained within 5295 iterations with an error=7.88x10-9. Figure 4 illustrates the closeness between the simulated and final values as well as the overlap between the simulated and final waveform.

-0.3 0

0.5

1 Time(ms)

1.5

2

Fig. 3. Loss of half of the fibres. Simulation and decomposition of 25 fibres for a test radius of 5 mm. No. of iterations=2500 (simulated blue ‘.’, forecasted red ‘-‘). The top graph shows the positioning of the fibres (simulated blue ‘o’, forecasted red ‘+’). IV. CONCLUSIONS The MUAP represents the spatial and temporal summation of SFEAPS generated from the same motor unit. MUAP morphology is affected by structural reorganisation of the motor unit that takes place due to disorders affecting peripheral nerve and muscle. MUAP features extracted in the time domain like duration, amplitude and phases are extensively used by the neurophysiologist for the assessment of neuromuscular disorders. In this study, a model based decomposition of MUAPS into their constituent SFEAPS has been investigated. Initially, the two-dimensional mathematical model developed by Dimitrova and Dimitrov [1], [2] has been used to generate SFEAPS. The two dimensional model was then extended to a three-dimensional model. Typical MUAPS

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M.G. Xyda, C.S. Pattichis, P. Kaplanis, C. Christodoulou and D. Zazula

These findings suggest that an “estimated” visualization of the structural organisation of the motor unit can be derived. This visualization gives to the neurophysiologist information about the number of fibres, fibre distribution and positioning and fibre diameter for the simulated cases investigated. A main limitation of the method proposed is that the initial values given should be very close to the targeted values for convergence to be achieved. Future work will investigate the usefulness of the proposed new method on MUAPS recorded from normal subjects and subjects suffering with neuromuscular disorders.

3 2 z mm

1 0 -1 -2 -3 4 2 0 -2 -4

y mm

-4

-2

2

0

4

x mm

ACKNOWLEDGMENT Simulated . Forecasted 1.5

This study was partly supported via the Cyprus-Slovenia CY-SLOV/0605 APASHM project, funded by the Research Promotion Foundation of Cyprus and the Corresponding Slovenian Research Authority. The authors would also like to thank Prof Dimitrova and Prof Dimitrov from the Bulgarian Academy of Sciences for their help at the early stages of her work in selecting and implementing the SFEAP model.

EAP(mv)

1

0.5

0

-0.5

REFERENCES -1

0

0.5

1 Time(ms)

1.5

2

Fig. 4. Reinnervation of fibres. Simulation and decomposition of 75 fibres

1.

for a test radius of 5 mm. Twenty five fibres were reinnervated in the right plane of the MU territory. No. of iterations=5295 (simulated blue ‘.’, forecasted red ‘-‘). The top graph shows the positioning of the fibres (simulated blue ‘o’, forecasted red ‘+’).

2.

recorded from normal and myopathic muscle have been simulated. The non linear least squares optimization procedure based on Levenberg-Marquardt algorithm has been used to obtain a solution to the MUAP decomposition problem i.e. fibre distribution and positioning and fibre diameter. Two-dimensional and three-dimensional models for normal MUAPS consisting of 1, 4, 5, 10 and 50 fibres respectively were developed and decomposed for a small recoding radius up to 5 mm. Also three-dimensional models have been developed and decomposed for the myopathic cases (loss of fibres, variability in the diameter and reinnervation). In all the models investigated, satisfactory results have been obtained within a rather limited number of iterations.

4.

3.

5.

6. 7. 8.

Dimitrova NA (1974) Model of the extracellular potential field of a single striated muscle fibre. Electromyogr Clin Neurophysiol 14:5366 Dimitrov GV (1987) Changes in the extracellular potentials produced by unmyelinated nerve fibre resulting from alterations in the propagation velocity of the duration of the action potential. Electromyogr Clin Neurophysiol 27:243-249 Graupe D, Vern B, Gruener G, et al. (1988) Decomposition of Surface EMG Signals into Single Fiber Action Potentials by Means of Neural Networks, IEEE Intern Symposium on Circuits and Systems 10:1008-1011. LeFever RS, Xenakis AP and De Luca CJ (1982) Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials – Part II: Execution and Test for Accuracy, IEEE Trans Biomed Eng. vol. BME-29, 3:158-164 LeFever RS and De Luca CJ (1982) Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials – Part I: Technique, Theory, and Implementation. IEEE Trans Biomed Eng. vol. BME-29, 3: 149-157 McGill KC and Dorfman LJ (1985) Automatic Decomposition Electromyography (ADEMG): Validation and Normadive Data in Brachial Biceps. Electroencephalogr Clin Neurophysiol 61:453-461 Schoonoven R, Stegeman DF, van Oosterom A et al. (1988) The Inverse Problem in Electromyography –I: Conceptual Basis and Mathematical Formulation. IEEE Trans Biomed Eng 35:769-776. Nandedkar SD, Sanders DB and Stalberg EV (1988) EMG of reinnervated motor units: a simulation study. Electroencephalogr Clin Neurophysiol 70: 177-184, 1988.

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Model Based Decomposition of Muaps Into Their Constituent Sfeaps 9.

Dubowitz V, Brooke M (1973) Muscle Biopsy: A Modern Approach. W.B. Saunders, Philadelphia 10. Aquilonius S, Askmark H, Gilberg P, et al. (1984) Topographical localization of motor endplates in cryosections of whole human muscles. Muscle & Nerve 7:287-293 11. Gill PR, Murray W. and Wright MH (1981) The Levenberg– Marquardt Method.§4.7.3 in Practical Optimization, Academic Press, London, 1981, pp 136–137

123 Address of the corresponding author: Author: Institute: Street: City: Country: Email:

Constantinos Pattichis Department of Computer Science, University of Cyprus Kallipoleos 75, P.O. Box 20537 CY1683 Nicosia Cyprus [email protected]

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Non-invasive estimation of the degree of motor unit synchronization in the biceps brachii muscle A. Holobar1, M. Gazzoni1, D. Farina2, D. Zazula3, and R. Merletti1 1

2

LISiN, Politecnico di Torino/Department of Electronics, Torino, Italy Center for Sensory-Motor Interaction, Aalborg University/Department of Health Science and Technology, Aalborg, Denmark 3 University of Maribor/Faculty of Electrical Engineering and Computer Science, Maribor, Slovenia

Abstract— This paper addresses estimation of motor unit (MU) synchronization by means of surface electromyogram decomposition. Firstly, the so called convolution kernel compensation method for identifying the discharge patterns of individual motor units is briefly described. The method builds on independent component analysis framework and is, hence, highly robust to superimpositions of MU action potentials (AP). In this study, the method was tested with synthetic signals in presence of MU synchronization, when the MU discharges are not strictly independent anymore. The level of MU synchronization was measured by the cross-interval histograms of reconstructed MU discharge patterns. Two synchronization indices were computed: the average number of synchronized MU pairs per contraction and the average percentage of synchronized AP per synchronized MU pair. Results revealed that both indices vary substantially with the properties of reconstructed MUs, but can nevertheless be used as reliable estimators of MU synchronization. The method was then applied to experimental electromyographic signals, acquired during low force contractions of the dominant biceps brachii muscle. Up to 14 concurrently active MUs were identified. MU synchronization was observed in approx. 50% of MU pairs. It is thus concluded, that MU synchronization can be assessed by decomposing the surface electromyogram. Keywords— surface electromyogram, motor unit synchronization, convolution kernel compensation, cross-interval histogram, biceps brachii muscle.

I. INTRODUCTION The ability of motor units (MUs) to discharge asynchronously has been commonly recognized as crucial for producing smooth muscle forces. Moderate MU synchronization, however, has been experimentally confirmed by numerous studies, which were mainly based on intramuscular EMG [5, 6]. It has also been proven by indirect measures on surface EMG, but these measures are often affected by the volume conductor properties [8, 9]. Recently, high-density (HD) surface EMG electrode grids and multi-channel amplifiers have become available. This technology allows detection of hundreds of surface EMG signals from closely spaced electrodes over a single muscle [1]. Different decomposition techniques for identification of individual motor units with these recording sys-

tems have also been proposed. Holobar & Zazula [2] presented a Convolutive Kernel Compensation (CKC) decomposition approach. It has been proven that this technique extracts almost complete MU discharge patterns of relatively large samples of active MUs from high-density surface EMG. Its performance, however, was measured only in the case of isometric, low-force contractions, with uncorrelated MU discharge patterns [2], as assumed for the derivation of the CKC. Therefore, the aim of this study was to assess the capability of the CKC method to estimate the level of MU synchronization from the decomposition of the surface EMG. II. MATERIALS AND METHODS A. Synthetic surface EMG signals The simulations were based on a model of recruitment of a population of motor units [3] and a volume conductor model [4]. The volume conductor was cylindrical with bone, muscle, subcutaneous, and skin tissues. The simulations comprised four main steps: 1) determining the recruitment and discharge times of a population of motor neurons, 2) synchronizing the generated discharge patterns, 3) generating MU action potential (MUAP) and, 4) generating MUAP trains and summing them into surface EMG signals. Motor unit action potentials. The cylindrical volume conductor model consisted of bone, muscle, fat and skin layer. The simulation of the intracellular action potential was based on the analytical description of Rosenfalk [7]. A set of 200 MUs were simulated in a region of the muscle of 200 mm2 cross-section. The distribution of the MU locations was random and the fibers of a MU were randomly scattered in the MU territory which was circular, with a density of 20 fibers/mm2, and intermingled with fibers belonging to other MUs. Innervation numbers ranged from 25 to 2500 with an exponential distribution. The surfacerecorded MUAP comprised the sum of the action potentials of the muscle fibers belonging to the MU. The MUs had muscle fiber conduction velocities of 4 ± 0.3 m/s.

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The recording system was a grid of 13×5 electrodes of circular shape (1-mm radius) with 5-mm interelectrode distance in both directions. The center of the grid was located 15 mm from the innervation zone location. A bipolar recording was simulated for each longitudinal pair of consecutive electrodes, thus leading to 60 simulated signals. The signals were additionally corrupted by additive zeromean Gaussian noise, with 20-dB SNR. Discharge pattern. A contraction at 5% excitation level (constant over time) was simulated. The distribution of recruitment thresholds for the motor neurons was represented as an exponential function with many low-threshold neurons and progressively fewer high-threshold neurons. With this choice the number of active MUs at 5 % excitation was 73 out of 200. The discharge rate at recruitment and the peak discharge rate were 8 and 35 pulses per second (pps), respectively. The last MU was recruited at 80% of maximal excitation. Variability in discharge rate was modelled as a Gaussian random process with a coefficient of variation of 20%. Independently generated discharges of each MU were pair-wise synchronized with the nearest discharges of other MUs. Synchronization was performed in such a way that every MU served as a reference MU to which the discharges of SMU remaining MUs were adjusted. Firstly, for each reference MU, SMU MUs were randomly selected to be synchronized. Special care was paid to already assigned synchronization pairs (if MU n was chosen to be synchronized with MU m, then MU m was also chosen to be synchronized with MU n). For each selected MU, first-order recurrence times (i.e. the time distances of the reference MU discharge to the nearest discharge of selected MU) were measured. Finally, SAP discharges with the smallest recurrence times were chosen per each selected MU and aligned to the nearest discharges of the reference MU. In order to simulate physiological variability of MU synchrony, the synchronized MU discharges were not perfectly aligned. Instead, normally distributed time delay with mean of 0 ms and std. dev. of 1.5 ms was introduced [5]. The portion of synchronized MU discharges SAP ranged from 0 % to 20 % (in steps of 5 %), while SMU was set to 25%, 50% and 75% of all the active MUs, respectively. Ten simulation runs per each SAP and SMU pair were performed. B. Experimental protocol Five young healthy male subjects (age: 27.0 ± 2.1 yr; stature 1.79 ± 0.09 m; body mass 73 ± 8 kg) participated to the experiment. The local ethics committee approved the study and all subjects signed an informed consent form. Surface EMG signals were detected with a matrix of 61 electrodes arranged in 5 columns and 13 lines (without the

A. Holobar, M. Gazzoni, D. Farina, D. Zazula and R. Merletti

four corner electrodes). Inter-electrode distance was 5 mm. The electrode pins (diameter 1.27 mm; RS 261-5070, Milan, Italy) were telescopic to adapt to the skin surface. The matrix was connected to four 16-channel EMG amplifiers (LISiN; Ottino Bioengineering, Rivarolo (TO), Italy). Recordings were performed in single differential configuration during 5 min long isometric constant-force contractions (at 5 % and 10 % of the maximal voluntary contraction force, MVC) of the dominant biceps brachii muscle (the dominant arm of the subject was placed into the isometric brace at 120°). The EMG signals were amplified, band-pass filtered (3 dB bandwidth, 10 Hz-500 Hz), sampled at 2500 Hz, and converted to digital form by a 12 bit A/D converter. The force signal was measured by a torque sensor (mod. TR11, CCT Transducers, Torino, Italy), provided as feedback to the subject on an oscilloscope, and recorded for further analysis. For technical reasons, the EMG recordings were divided into 30 s long epochs. C. Signal decomposition and analysis The CKC method [2] was applied for identification of MU discharge patterns. The method fully automates the reconstruction of MU discharge sequences and is highly resistant to noise and MUAP superimpositions. The level of synchronization between the different pairs of identified MU was analyzed by cross-interval histogram [5], which measures distribution of the first-order forward and backward recurrence times (i.e. the time distances of the reference MU discharge to the nearest forward and backward discharge of other MU) [5]. In the case of no interdependence among both MUs, the cross-interval histogram should appear as a flat line, reflecting the uniform probability density function. This would imply the identified recurrence times have the equal probability to fall into any of the histogram bins: p = BW , HW

(1)

where p is the probability of occurrence in a given histogram bin, BW is the width of a histogram bin (2 ms in our case), while HW is the histogram width (i.e. time period spanned by the histogram). In our case, the histogram was limited to the interval [-mRT,+mRT], where mRT is maximum recurrence time of reference MU. By modelling the occurrence of a recurrence time in a selected histogram bin as a Bernoulli random event, the probability of having n or more occurrences in a bin may be expressed as follows [5]: Nr N ⎛ ⎞ P (n) = ∑ ⎜ r ⎟ pi (1 − p) N r −i , i =n ⎝ i ⎠

(2)

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Non-invasive estimation of the degree of motor unit synchronization in the biceps brachii muscle

•

Sˆ AP = ⎣⎡ 2 ⋅ ( peak area − mean area ) / ( total area ) ⎦⎤ ⋅100 % (3)

where peak area stands for the total number of occurrences in a peak, mean area is the number of occurrences in the peak that are under the mean histogram level, while total area is the number of all occurrences in the histogram. Typical cross-interval histogram demonstrating the MU synchronisation is exemplified in the right plot of Fig. 3. III. RESULTS A. Synthetic signals On average, 5 ± 2 MUs were reconstructed from each set of simulated signals. The sensitivity and specificity of reconstructed MU discharge patterns are depicted in Fig. 1. Level of synchronization was quantified by the crossinterval histogram analysis. Observed synchronization peaks were centred on the zero-delay position, with the average peak width ranging from 2 ms at SAP = 5% to 6 ms at SAP = 20 %. Measured MU synchronisation index ŜMU, and AP synchronisation index ŜAP are depicted in Fig. 2. B. Experimental signals On average 8.8±4.4 (5 % MVC contraction) and 9.6±3.8 (10 % MVC contraction) concurrently active MUs were identified out of acquired signals (Table 1). This allowed for comparison of 45 concurrently active MUs pairs, on average. Statistically significant synchronization peaks were detected in 50 % of the investigated MU pairs. No statistically significant differences for the measured synchroniza-

No. of MUs Sensitivity [%]

• •

Specificity [%]

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peak width defined as Np·BW, where Np is the number of neighbouring bins exceeding the 99 % confidence level; peak location: the time location of the median of all the occurrences in the peak; number of consecutive synchronous MU discharges; MU synchronisation index ŜMU, defined as the ratio between the number of MU pairs exhibiting synchronisation and the total number of tested MU pairs; and pulse synchronisation index defined as

SMU = 25 %

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tion parameters were observed among different 30 s long signal epochs and also among different contraction levels (Kurskal-Wallis ANOVA test, p>0.05). Peak width ranged from 1 ms to 5 ms, with the mean value of 1.5 ms. The majority of the peaks were within 6 ms from the central, zero-delay position. Although significant, the number of synchronized MU discharges was generally small, varying between 3 % and 9 % of all MU discharges (Fig. 4, left bottom panel).

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where Nr stands for the total number of reference MU discharges. The testing for the existence of synchronisation can, hence, be performed by calculating the confidence level with which it can be established that the detected peak in the cross-interval histogram is not a random event. In this study the confidence level was set at 99 %. For each statistically significant peak, the following parameters were calculated [5]:

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Fig. 2 Estimated MU synchronization ŜMU (top panel) and estimated AP synchronization ŜAP (bottom panel) vs. the simulated levels of MU synchronization SMU and AP synchronization SAP.

IV. DISCUSSION Results on synthetic signals demonstrated that the CKC method is readily capable of decomposing surface EMG signals in the presence of MU synchronisation. No significant impact of the number of synchronized MUs and of the portion of synchronized APs on the decomposition was noticed (Fig. 1). On the other hand, high inter-trail variance of both synchronisation indices ŜMU and ŜAP was observed. This is due to the fact that only up to 10 superficial and large MUs were identified, while all other MUs were considered as background noise. Both performance indices

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measure the level of synchronisation in the reconstructed subset of MUs only and do not always yield representative results. In addition, measured level of MU synchronization ŜMU depends also on the level of simulated AP synchronization. Nevertheless, both ŜMU and ŜAP indices can still be considered reliable indicators of MU synchronisation as they both consistently disclose the cases with no MU synchronisation present.

Table 1 Number of MUs (mean ± std. dev.), their MU synchronization index ŜMU and AP synchronization index ŜAP. The results are obtained from experimental SEMG recordings and averaged over all epochs.

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The results of this study demonstrated that the direct assessment of MU synchronization from surface EMG signals is possible. By identifying up to 14 concurrently active MUs per contraction in experimental conditions, synchronization in up to 91 MU pairs was studied. To our knowledge, no other study exists, which would noninvasively compare such a large number of concurrently active MUs.

ACKNOWLEDGMENT

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MU 3(Nr=483) vs. MU 6(Nr=404)

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In the case of experimental signals, moderate MU synchronisation was observed (Table 1). The confidence level was set to 99 %, implying that the probability of observing a histogram peak due to the random chance was less than 1%. Synchronisation peaks were detected in 50 % of all the tested MU pairs. Therefore, the identified synchronisation peaks were considered reliable indicators of true MU synchronisation. Another relatively strong evidence of MU synchronisation is presented in Fig. 4, where the typical distribution of peak locations in the histogram is depicted. If the detected peaks were due to the random chance, their locations would be uniformly distributed across the histogram. This is clearly not the case.

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Fig. 3 Cross-interval histogram without (left panel) and with statistically significant synchronization peak (right panel). Light and dark gray dashed lines represent 95 % and 99 % confidence levels, respectively. Nr stands for the number of MU discharges, identified from real surface EMG signals (Subject B, 5 % MVC contraction, epoch 1).

This work was supported by the European Commission within the Sixth Framework (Project Cybermans) and Marie Curie Intra-European Fellowships Action (DE MUSE, Contract No. 023537).

REFERENCES

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Fig. 4 Distribution of synchronization peak widths (upper left panel), locations (upper right panel), AP synchronization index (lower left panel) and number of consecutive synchronized MU discharges (lower right panel) averaged over all signal epochs (Subject B, 5 % MVC contraction).

3. 4. 5.

Merletti R, Parker PA(2004) Electromyography: physiology, engineering, and non-invasive applications, IEEE Press and John Wiley & Sons. Holobar A, Zazula D(2004) Correlation-based decomposition of surface EMG signals at low contraction forces, Med. & Biol. Eng. Compt, 42:487-496. Fuglevand AJ, Winter DA, Patla AE.(1993) Models of recruitment and rate coding organization in motor unit pools. J. Neurophysiol; 70:2470–88. Farina D, Merletti R (2001) A novel approach for precise simulation of the EMG signals detected by surface electrodes, IEEE trans. Biomed. Eng., 48:637-646. De Luca CJ, Roy AM, Erim Z(1993) Synchronization of Motor-unit Firings in Several Human Muscles, J. of Neurophysiol., 70:2010-23.

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Nordstrom MA, Fuglevand AJ, Enoka RM (1992) Estimating the strength of common input to human motoneurons from the cross-correlogram, J. of Physiol., 453:547-74. Rosenfalck P. (1969), Intra- and extracellular potential fields of active nerve and muscle fibers, Acta Physiol. Scand. Suppl., 47:239-246. Keenan K.G., Farina D., Merletti R., Enoka R.M. (2006) Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG”, J. Appl. Physiol., 100: 1928-37.

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Keenan KG, Farina D, Meyer F, Merletti R, Enoka RM. (in press) Sensitivity of the cross-correlation between simulated surface EMGs for two muscles to detect motor unit synchronization, J. Appl. Physiol. Address of the corresponding author: Author: Institute: Street: City: Country: Email:

Ales Holobar LISiN, Politecnico di Torino Via Cavalli 22/H Torino Italy [email protected]

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Abdominal EHG on a 4 by 4 grid: mapping and presenting the propagation of uterine contractions B. Karlsson1, J. Terrien1, V. Gudmundsson2, T. Steingrimsdottir2 and C. Marque3 1

Reykjavik University/Department of biomedical engineering, Reykjavik, Iceland 2 Landspitali University hospital/Dept. ob-gyn, Reykjavik Iceland 3 UTC/Department of biomechanics and biomedical engineering, Compiegne, France Abstract— Numerous studies have observed and analyzed the external electrical activity of the uterus associated with contractions and labor. Most of these studies have involved the use of only 3 to 5 electrodes and little effort has been made to investigate the electrical activity concurrently at different locations. In this paper we present the results from measurements of contractions in labor using a 16 electrodes grid. We tried out various methods of presenting and analyzing this data and found this to be a non-trivial task. Here we present both an animation of the evolution of the electric potential, as well as a temporal correlation presentation. The results from a limited sample are in many ways surprising and may provide a new insight in to possible mechanism underlying uterine contractions. Keywords— EHG, labor, uterus, mapping, propagation

I. INTRODUCTION Premature labor is one of the most important public health problems in Europe and other developed country as it represents nearly 7% of all births. It is the main cause of morbidity and mortality of newborns. Early detection of a preterm labor is important for its prevention for example in insuring tocolytic drug efficacy. Continuous efforts are made to find new biochemical or biophysical markers of preterm labor threat [1]. One of most promising is the analysis of the electrical activity of the uterus. Uterine electromyogram recorded externally in women, the so called electrohysterogram (EHG), has been proved to be representative of uterine contractility. The analysis of such signal may allow the prediction of a preterm labor threat as soon as 28 weeks of gestation (WG) [2-4]. However, the physiological phenomena underlying preterm labor remain badly understood. It is well known that the uterine contractility depends on the excitability of uterine myocytes but also on the propagation capability of local electrical activity to the whole uterus [5]. These two aspects of uterine contractions mechanisms, excitability and propagation, both influence the spectral content of EHG. EHG is mainly composed of two frequency components traditionally referred to as FWL (Fast Wave Low) and FWH (Fast Wave High) [5]. These frequency components may be

related to the propagation and the excitability of the uterus respectively. Recent studies on the early prediction of preterm labor focused on the analysis of FWH or simply the high frequencies of the EHG. If the above hypothesis of FWH being primarily related to the local excitability of the uterus is correct, the mechanisms of coordination and organization of the uterus as a whole has still not been fully understood or exploited in predicting labor. The propagation of the electrical activity of the uterus has been studied both at a cellular level and on the organ as a whole. The propagation at a cellular level shows complex activation pathways with the possible presence of multiple fronts or re-entry like in the heart [6]. Investigation of the propagation at the organ level has only been done using 3 to 5 recording electrodes. The propagation speed observed is very dependent on the species but the average estimated speed was typically superior to 2 cm/s as reported by [7, 8] for example. Planque describes a speed, calculated on abdominal EHG recorded in woman, of 2.18 cm/s and of a propagation in a descending direction in 87% of cases [9]. Duchêne et al. also noticed a constant chronogram of the activation pattern during labor in monkeys [10]. The main method of EHG propagation analysis has so far been linear intercorrelation. In reported work the intercorrelation coefficients calculated on EHG envelope are usually good (~80%) but the coefficients calculated on temporal signals are much lower. This could depict a good group propagation (sequential activations of several uterine regions) but not a strict linear propagation between each recording regions as frequently observed in striated muscles. Other more sophisticated analysis tools have been used but were unable to properly demonstrate linear propagation [9-11]. The aim of this paper is to study the propagation of the uterine electrical activity recorded on women during labor. We present a specific recording methodology using 16 monopolar electrodes and the results of preliminary measurements. We show examples and group propagation analysis tools.

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II. MATERIALS AND METHODS A. Instrumentation and experimental protocol The measurements were performed by using a 16channel multi-purpose physiological signal recorder most commonly used for investigating sleep disorders (Embla A10). Reusable Ag/AgCl electrodes were used. The measurements were performed at the Landspitali University hospital in Iceland using a protocol approved by the relevant ethical committee (VSN 02-0006-V2). The subjects were healthy women in the first stages of labor having uneventful singleton pregnancies. After obtaining informed consent, the skin was carefully prepared using an abrasive paste and alcoholic solution. After that, the sixteen electrodes were placed on the abdominal wall according to Fig. 1 (interelectrode distance: 2.1 cm). The third electrode column was always put on the uterine median axis and the 10-11th electrode pair on the middle of the uterus (fundus to symphysis). Reference electrodes were placed on each hip of the woman. The signal sampling rate was 200 Hz. The recording device has an anti-aliasing filter with a high cut-off frequency of 100 Hz. The tocodynamometer paper trace was digitalized in order to facilitate the segmentation of the contractions. In this preliminary study, two women in spontaneous labor were enrolled at 37 and 39 WG. The recording duration was approximately 1 hour in both cases. 23 and 21 contractions were clearly identified on these. In our study, we considered vertical bipolar signals (BPi) in order to increase the signal to noise ratio. Our signals form thus a rectangular matrix of size 3 x 4. All the EHG bursts presented a good signal to noise ratio on all of the bipolar channels. All the EHG bursts were segmented manually with the help of the tocodynamometer trace.

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After a manual segmentation of each contraction, the envelope of each bipolar EHG signal was calculated. The envelope was defined as the modulus of the analytical signal obtained by Hilbert transform. The different envelopes were then filtered by a moving average filter. Propagation animation: This involved representating the evolution of the envelope amplitude on each bipolar signal as a function of time. For each time n, the envelope amplitude was display in an arbitrary color scale. All amplitudes were normalized by the maximal amplitude obtained for each of the different channels. In order to obtain a smoother representation, the 3 x 4 amplitude matrix was interpolated to 9 x 13. To facilitate the interpretation of such representation, the gradient information (amplitude and angle) was superimposed. Correlation analysis: The most common way to analyze the delay between two signals, x(n) and y(n), is the use of the intercorrelation function Ø(k). This function presents a maxima for a value K0 corresponding to the delay T0 between the two signals. The maxima position gives information on the delay but also on the propagation direction. We could have three distinct situations: • If 0 < K0 < (N/2+1), then x(n) is in advance of: T0 = K0/Fs seconds on y(n) • If (N/2) < K0 < N then y(n) is in advance of: T0 = (N - K0)/Fs seconds on x(n) • If K0 = 0 then x(n) and y(n) appeared simultaneously Where Fs is the sampling frequency and N the length of each signal. The intercorrelation function can be obtain by the Fourier transform (FT) of each signal. The FT of the intercorrelation function is then:

Φ ( f ) = X ( f )Y * ( f ) Where X(f) and Y(f) are the FT of x(n) and y(n) respectively and * indicates complex conjugate. The intercorrelation function is then obtained by inverse Fourier transform. A normalization of Ø(k) by the energy of the two signals guaranties that this function is bounded between 0 and 1. All delays were calculated with the envelope of BP8 as reference signal x(n). The delay matrix were interpolated linearly in order to obtain a smoother representation.

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Fig. 1: Electrode configuration on the woman abdominal wall and position of bipolar signals Bpi.

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III. RESULTS A. Envelope amplitude animation The observation of the temporal animation of the envelope amplitude is realized by the interface presented Fig. 2. This interface shows the tocodynamometer trace, the mean EHG envelope and the envelope amplitude for each channel spatially. These animated maps of uterine electrical activity show a surprising amount of structure. At each location (electrode pair) several bursts of activity can be observed at different times in each contraction as the potential sweeps across the uterus. The animations are very complex to analyze. We have however been able to notice some particular situations. It is possible at times to observe ascendant activation patterns while for the majority of the contractions the activation patterns is descendant. In this situation, the uterine activity begins at the lower electrodes or those situated on one side and then propagate to the other electrodes. Several origins of the activity could often be observed (Fig. 3). Some of the individual envelopes do not present their maximal value at the maximal amplitude of the mean EHG envelope (Fig. 4). The maximum of the EHG envelopes are usually observed close to the maximum of the tocodynamometric trace but are not synchronized to each other. It indicates that, even in labor, a delay between each channel is observed. Moreover, rotating activation patterns have been noticed during the same contraction. For one contraction, the presence of a pace-maker-like node that apparently initiated the whole contraction was observed in that the electrical activity

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Fig. 3: Example of the presence of several origins of the activity and ascendant activation pattern. The gradient information is indicated by arrows.

Fig. 4: Envelope amplitudes obtained at the maximal value of the mean envelope. The gradient information is indicated by arrows.

begins on one EHG channel then propagates to other channels. Several animations can be found at http://www.ru.is/brynjar/ehg. B. Correlation analysis

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The correlation analysis gives information on the global delay between all EHG channels during a contraction. The quality of the correlation between channels was also evaluated by calculating the correlation coefficients. This analysis permits to better reveal the presence of a pacemaker-like activity. A point of origin could be easily seen in Fig. 5, were an isolated area with a negative delay is observed. The corresponding correlation coefficient are relatively good indicating a true physiological phenomena (> 75 %).

of each EHG channel (BP1 to BP12). The upper trace represents the normalized tocodynamometer trace (Toco.) and the normalized mean envelope (MEnv.) calculated on each channel (x axis in second).

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Our analysis of the evolution of the envelope amplitude during contraction showed a non linear propagation with complex activation patterns. Ascendant or rotating activation patterns were observed. It has been proposed that pace-maker cells are present preferentially near the uterine fundus [7]. It is however well known that all uterine cells can become a pace-maker cell. The electrical activity can thus be initiated anywhere on the uterus. The rotating pattern can be explained by re-entry phenomenon where the electrical activity returns to its initiation site after a certain time. This interpretation is unfortunately only qualitative as of yet. A more detailed characterization of the activation patterns has to be done in order to observe how it evolves as the uterus organizes itself from the quiet state during most of the pregnancy to the very active coordinated state during the final stages of labor. Intercorrelation analysis of the EHG envelopes is an example of how quantitative information can be extracted from this type of data. It indicates the global delays between the electrical burst present on each recording channel. The quality of the parameter can also be checked by the different correlation coefficients, providing a new way of identifying effective contractions. The study of the different delays obtained for one contraction reveals clearly the presence of a pacemaker-like initiator cells under the electrodes. We have not observed a constant chronogram or activation pattern for successive contractions like reported by [10]. However, in our study, the inter-electrode distance is shorter and thus could reflect a more local propagation pattern. Other correlation analysis tools have to be evaluated in order to extend our preliminary results and maybe to increase the accuracy of our analysis. For example, the linear correlation coefficients calculated on temporal EHG were very low [9], the use of non-linear correlation techniques may be more suitable for this signal. An other important perspective of this work is the confirmation or the rejection of the hypothesis, stated by Devedeux et al [5], on the link between FWL and the propagation on one hand and FWH and excitability on the other hand. Analysis of the correlation of the energy of each frequency component separately could answer this open question.

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IV. DISCUSSION

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Fig. 5: Estimated delays (second) obtained, for one contraction, by intercorrelation. A higher interpolation was realized to increase the readability.

energy of the 12 bipolar EHG permitted us to observe complex activation patterns. These animations and the use of EHG intercorrelation mapping may also show the presence of pace-maker activity or re-entry like those observed in the heart. Our methodology may eventually help to characterize various obstetrical situations and even provide tools useful in managing preterm labor. REFERENCES 1. 2.

3.

4.

5. 6. 7.

V. CONCLUSIONS In this paper, we present a recording for device with 16 monopolar electrodes. The possibility of a mapping the electrical activity of the uterus is likely to give new information on the propagation of activity in the organ. The animation of the spatial and temporal evolution of the

8. 9.

J. Terrien, C. Marque, and G. Germain, "What is the future of tocolysis?," Eur J Obstet Gynecol Reprod Biol, vol. 117 Suppl 1, pp. S10-4, 2004. H. Leman, C. Marque, and J. Gondry, "Use of the electrohysterogram signal for characterization of contractions during pregnancy," IEEE Trans Biomed Eng, vol. 46, pp. 1222-9, 1999. R. E. Garfield, H. Maul, L. Shi, W. Maner, C. Fittkow, G. Olsen, and G. R. Saade, "Methods and devices for the management of term and preterm labor," Ann N Y Acad Sci, vol. 943, pp. 203-24, 2001. R. E. Garfield, H. Maul, W. Maner, C. Fittkow, G. Olson, L. Shi, and G. R. Saade, "Uterine electromyography and lightinduced fluorescence in the management of term and preterm labor," J Soc Gynecol Investig, vol. 9, pp. 265-75, 2002. D. Devedeux, C. Marque, S. Mansour, G. Germain, and J. Duchene, "Uterine electromyography: a critical review," Am J Obstet Gynecol, vol. 169, pp. 1636-53, 1993. W. J. Lammers, "Circulating excitations and re-entry in the pregnant uterus," Pflugers Arch, vol. 433, pp. 287-93, 1997. R. Caldeyro-barcia and J. J. Poseiro, "Physiology of the uterine contraction," Clin. Obstet. Gynecol., vol. 3, pp. 386408, 1960. E. E. Daniel and S. A. Renner, "Effect of the placenta on the electrical activity of the cat uterus in vivo and in vitro," Am J Obstet Gynecol, vol. 80, pp. 229-44, 1960. S. Planque, "Contribution a l'etude de la propagation des signaux electrohysterographiques," in Genie biomedical. Compiegne: Universite de technologie de Compiegne, 1990.

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Abdominal EHG on a 4 by 4 grid: mapping and presenting the propagation of uterine contractions 10. J. Duchene, C. Marque, and S. Planque, "Uterine EMG signal: Propagation analysis," presented at Annual International Conference of the IEEE EMBS, 1990. 11. S. Mansour, "Etude de l'electromyographie uterine : caracterisation, propagation, modelisation des transferts," in Génie biomedical. Compiegne: Universite de technologie de Compiegne, 1993.

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Author: Jeremy Terrien Institute: Street: City: Country: Email:

School of Science and Engineering, R.U. Kringlan 1 103 Reykjavik Iceland [email protected]

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Detection of contractions during labour using the uterine electromyogram D. Novak1, A. Macek-Lebar1, D. Rudel2 and T. Jarm1 1

University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Biocybernetics, Ljubljana, Slovenia 2 MKS Electronic Systems, Rozna dolina C. XVII/22b, 1000 Ljubljana

Abstract — This paper describes two simple algorithms for detection of uterine contractions during labour using the uterine electromyogram recorded from the abdominal surface. The location of a contraction is extracted from the signal’s energy using either an amplitude- or derivative-based algorithm. For our recordings, these algorithms managed to correctly locate the majority of contractions, with an average success rate of 87.4% for the derivative-based algorithm and 85.6% for the amplitude-based algorithm. Keywords — Uterus, contractions, electromyogram, signal processing.

I. INTRODUCTION Several methods for obtaining information concerning uterine contractions are in use today, but many of them have serious disadvantages. Intrauterine pressure catheters are invasive and can increase the risk of infection. Tocodynamometers are noninvasive, but fairly inaccurate [1]. The uterine electromyogram, on the other hand, is measured noninvasively and contains information about uterine activity. Not only does electrical activity increase as pregnancy progresses, changes also occur in the frequency domain. It has been proposed that observation of the EMG during labour may even allow prediction of preterm labour and abnormal birth [1,2]. We focused on a simpler task: creating an algorithm that can detect the contractions in a recording automatically. II. SIGNAL RECORDING AND PROCESSING The study involved 12 women undergoing normal labour. Intrauterine pressure (IUP) and the uterine electromyogram obtained from the abdominal surface in transverse and longitudinal directions were recorded during labour using the method described in [4]. The sampling frequency was 18.2 Hz. Five of the recordings were discarded due to major measurement errors. The initial parts of the recording (very early labour) and the parts close to birth were not analyzed at this stage. Two types of electromyographic activities for abdominal recording are observed: slow wave and fast wave. The slow wave's frequency content ranges from 0.014 Hz to 0.033

Hz, but it is generally obscured by the many artifact components. The fast wave is easier to discern and is located between 0.1 Hz and 3 to 5 Hz [2,3], so the signal was filtered with a fourth-order Butterworth band-pass filter. We found that the filter's lower cutoff frequency strongly affected contraction detection, so several different values were tested. Setting the lower cutoff frequency to 0.1 Hz was too low to filter out most of the low-frequency component, so the tested values were between 0.4 Hz and 2.5 Hz. The upper cutoff frequency of the band-pass filter was arbitrarily set at 4.5 Hz. It did not have a major effect as long as it was not set too low; lowering it from 4.5 Hz to 3.5 Hz did not cause a noticeable difference. III. DETECTION OF CONTRACTIONS We chose two methods of detecting contractions: detection of peaks in the signal's energy and detection of changes in the signal's mean or median frequency. Analysis of the signal's energy was done by computing the energy of a signal on a certain interval as the sum of the squares of all signal values on that interval. The length of this interval strongly affected detection, so intervals of 0.2, 1, 2, 4 and 8 seconds were tested, and 1 second was eventually selected as the most suitable interval length. The centers of two consecutive intervals were separated by one second. We found that two contractions can vary in peak energy by a factor of as much as 10, so the signal energy was examined on a logarithmic scale for easier detection. The logarithm of signal energy (log(EEMG)) was again smoothed using a fourth-order Butterworth low-pass filter (with different tested cutoff frequencies) for easier detection. All these steps are shown on Fig. 1. Analysis of the signal's mean and median frequencies was done by calculating the mean and median frequencies on a certain interval using the power spectral density (PSD) which was estimated with the periodogram method. The interval widths and the tested cutoff frequencies of the band-pass filter were the same as those used in calculation of signal energy. Subjective evaluation of both log(EEMG) and mean/median frequency showed that both could be used for detection of contractions. However, contractions seemed to

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 148–151, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

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became lower than the negative value of the threshold. This was the moment when the contraction began to »wear out«. Afterwards, the algorithm searched for the time when the absolute value of the derivative was once again smaller than the threshold. This was the moment when the contraction ended. For an example of a detected contraction, see Fig. 2. B. Amplitude-based detection

Fig. 1 Two uterine contractions. a – intrauterine pressure, b – raw EMG, c – energy of EMG, d - log(EEMG) before smmothing, e - log(EEMG) after smoothing

stand out more prominently in log(EEMG), so we decided to create a detection algorithm based on signal energy in the time domain. Since contractions appeared as peaks in the signal energy, two different criteria could be used for detection: the amplitude and the derivative of log(EEMG). Thus, we built two different algorithms: one based on the amplitude and the other on the derivative. A. Derivative-based detection The first derivative of log(EEMG) is expected to be extremely positive at the beginning of the contraction and extremely negative toward the end of the contraction. It was calculated as the difference of two consecutive values of log(EEMG). Then, we also calculated its absolute value. The threshold for the beginning of the contraction was set as the average value of the absolute derivative (over the entire signal) multiplied by a constant. Once the first derivative became larger than this value, the beginning of a contraction was registered. The algorithm then searched for a time when the value of the derivative

Fig. 2 log(EEMG) as a function of time. The derivative-based algorithm locates the beginning of the contraction, the moment when the contraction begins to »wear out« and the end of the contraction (the 1st, 2nd and 3rd marking, respectively).

In log(EEMG), the peaks that occur during contractions are not all the same size; some are significantly higher than others. These differences can be greatly reduced by subtracting the moving average from log(EEMG). Intervals of several different lengths were tested for the calculation of the moving average. After subtracting the moving average, the algorithm calculated a threshold as a percentage of the maximum value of log(EEMG) over the entire signal. The beginning of a contraction was the point when log(EEMG) became larger than this threshold. The contraction ended once log(EEMG) once again became lower than this threshold. C. Error checking After the algorithm extracted the location of the contractions from the recording, it checked its own results for errors. The algorithm discarded contractions that were too short, joined two contraction intervals together if they occurred immediately one after another, and (for the derivative-based algorithm) discarded intervals on which the difference between the maximum and minimum value of log(EEMG) was too small. D. Evaluation of the algorithm's performance To determine the optimal settings for detection of contractions, the algorithm's results need to be evaluated. As contractions are easy to extract from the recording of intrauterine pressure, the best way to evaluate the algorithm's effectiveness is to compare the contractions found in the EMG with the contractions found in the IUP. To extract the location of the contractions from the IUP, the signal was first smoothed with a Butterworth low-pass filter, and then a simple amplitude-based algorithm was used for detection. We considered a contraction to have been successfully detected if either 75% of the contraction interval from the IUP lay inside the contraction interval from the EMG or 75% of the interval from the EMG lay inside the interval from the IUP. Additionally, if the interval from the EMG was more than twice as long as the interval from the IUP, the contraction was regarded as incorrectly detected.

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When comparing the results of our algorithm with the IUP, five parameters were defined:

•

• • • •

The derivative-based algorithm also used the following parameters:

•

The actual number of contractions based on IUP. (N) True Positives (TP): Correctly detected contractions. False Negatives (FN): Missed contractions. False Positives (FP): Incorrectly detected nonexistent contractions. Double Positives (DP): Two contractions detected as one.

Examples of detection errors are shown in Fig. 3. Double positives were counted separately since they may be a natural phenomenon: a period of time when the uterus's electrical activity remained high even between contractions, blurring two contractions into one in the EMG. However, any triple positives were most likely the result of an ineffective detection algorithm and were counted as three false negatives. For evaluation of the algorithm's performance we defined an additional value, the relative error rate, as: ERR = ( FN + FP + DP) / N

(1)

In our experiments, when we searched for the optimal settings of the detection algorithm, we chose those that resulted in the lowest error rate for a particular recording. First, we experimented with each parameter to find a range of values for that parameter that resulted in an acceptable error rate. Then we selected a number of possible values from this range. The algorithms were tested with every possible combination of these values in order to determine the optimal settings. The possible values used for both algorithms were as follows: •

Lower cutoff frequency of the filter for the raw EMG: Approximately [0.45, 0.675, 0.9, 1.125, 1.35, 1.575, 1.8, 2.025, 2.25] Hz

• •

Cutoff frequency of the low-pass filter used to smooth the log(EEMG): Approximately [0.025 0.035] Hz

Threshold for the detection of the beginning of the contraction: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] times the mean value of the absolute derivative Minimum difference between the maximum and minimum value of log(EEMG) on the interval of the contraction: [0.025, 0.05, 0.1, 0.15] times the maximum value of log(EEMG) in the entire recording

The amplitude-based algorithm also used the following parameters: • •

Length of interval used for calculation of the moving average of log(EEMG): [25, 50, 100, 200] seconds. Threshold for the detection of the beginning of the contraction: [0.3, 0.34, 0.38, 0.42, 0.46, 0.50, 0.54, 0.58] times the maximum value of log(EEMG) in the entire recording

Since the characteristics of the uterine EMG change with time, the seven recordings available were split into shorter ones (approximately 2.3 hours each), giving us ten recordings from seven different people. The algorithms were tested separately on each of these recordings. IV. RESULTS When using the optimal settings for each recording, both algorithms successfully detected a majority of the contractions (see Table 1). Table 1. Results using the best parameters for each recording

person recording

Fig. 3 Examples of detection errors when using the derivativebased algorithm. a – IUP, b – raw EMG, c - log(EEMG) with true positives and detection errors marked.

1-1 1-2 2-1 2-2 3-1 4-1 5-1 6-1 7-1 7-2 average

minimal ERR derivative-based amplitude-based 0.21 0.31 0.19 0.19 0.08 0.10 0.12 0.18 0.10 0.05 0.19 0.20 0.08 0.13 0.04 0.05 0.11 0.07 0.14 0.16 0.126 0.144

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The optimal settings varied greatly from recording to recording. We attempted to determine a combination of values that would be effective for a majority of recordings. The most effective combination would be the one that resulted in the lowest average error rate for all of the recordings. However, we were unable to find a combination of values that would provide an average ERR lower than 22.6% with either of the two algorithms. V. DISCUSSION A. Factors that affect the algorithm's effectiveness Measurement errors: The effectiveness of an automated contraction detection algorithm depends strongly on the quality of the recordings. Although any signals with obvious measurement errors were discarded prior to testing of the algorithm, we did not check the recordings for minor errors, and even small measurement errors can cause a noticeable change in the EMG energy. Physiological differences: Due to physiological differences, it is probably impossible to find fixed parameter values that would work effectively for all subjects. A possible solution would be to introduce a learning phase where the algorithm determines the most effective settings for each subject. B. Comparison of the amplitude-based and derivative-based algorithms At this moment it is difficult to say whether one algorithm is superior due to the relatively small amount of recordings used in the evaluation. For the derivative-based algorithm, the detected contraction intervals were often very long, with one beginning almost at the same time as the previous one ended (see Fig. 4). The amplitude-based algorithm produced narrower intervals, but had a larger amount of false negatives. A combination of the derivativeand amplitude-based approach may be the best solution. C. Conclusion Our algorithm can successfuly detect a large amount of uterine contractions from the electromyogram using a relatively simple approach. Modifications could likely increase its usefulness even further. For instance, an

Fig. 4 A comparison of the derivative- and amplitude-based algorithm's results. a – IUP, b – raw EMG, c - log(EEMG) with the contractions detected by the derivative-based algorithm, d - log(EEMG) with the contractions detected by the amplitude-based algorithm.

adaptive algorithm could detect contractions »on-line« during labour. In situations where invasive methods cannot be used to detect contractions, the uterine EMG can be a useful tool.

REFERENCES 1. 2.

3. 4.

Garfield R.E. et al. (2002) Uterine Electromyography and Lightinduced Fluorescence in the Management of Term and Preterm Labor. J Soc Gynecol Investig, vol. 9, no. 5, pp 265-275. Maner W., Garfield R.E. (2007) Identification of Human Term and preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Ann Biomed Eng. , vol. 35, no. 3., pp 465473. Khalil M., Duchene J. (2000) Uterine EMG Analysis: A Dynamic Approach for Change Detection and Classification. IEEE Trans Biomed Eng., vol. 47, no. 6, pp 748-756. Pajntar M. et al. (1987) Electromyographic observations on the human cervix during labor. Am J Obstet Gynecol, vol. 156, no. 3, pp 691-697.

Author: Institute: Street: City: Country: Email:

Alenka Macek-Lebar Fakulteta za elektrotehniko Trzaska cesta 25 Ljubljana Slovenia [email protected]

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Evaluating Uterine Electrohysterogram with Entropy J. Vrhovec1,2, A. Macek Lebar2, D. Rudel1 1

MKS Electronic Systems, Rozna dolina C. XVII/22b, 1000 Ljubljana University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana, Slovenia

2

Abstract— In this report, we evaluate complexity of the uterine EHG during labor by estimation of entropy. Two methods for entropy eva7luation have been chosen: approximate entropy and sample entropy. Our observation, based on three labors, that have been randomly chosen from our database, show that sample entropy indicates the course of labor. The values of sample entropy are higher during the latent phase then during the active phase of the labor. The complexity of uterine EHG signal is reduced in the active phase of labor. Sample values are also reduced after Oxytocin dose. Keywords— uterine electrohysterogram, approximate entropy, sample entropy.

I. INTRODUCTION

II. METHODS Approximate entropy (ApEn) and sample entropy (SampEn) were chosen, because they have been successfully applied for analyzing biological signals such as heart rate, blood pressure, electrocardiography, electroencephalography and electromyography [5, 6]. It was shown that ApEn can predict the sudden infant death syndrome (SIDS) [5]. SampEn analysis successfully showed the episodes of neonatal sepsis [6]. A. Approximate entropy ApEn was introduced as a quantification of complexity in sequences and time series data, initially motivated by applications to relatively short, noisy data sets [7, 8, 9]. Regular signals are expected to have low ApEn values [13], while complex ones take on higher ApEn values.

Physiological signals have a wide variety of forms. To describe them traditional feature measures typically extract amplitude and frequency information. This makes comparison of signals which have different bandwidths difficult. Given a signal{x(n)}={x(1), x(2), …, x(N)}, where N is When visually inspecting signals, one of the first impresthe total number of data points, ApEn algorithm can be sions they give to the observer is their complexity. Some summarized as follows: signals seem to vary more then others. Some appear extremely random while others seem to demonstrate a reap- I. Form N-m+1-vectors X(1) to X(N-m+1) define by : pearance of certain patterns at various intervals. In medical X(i)=[x(i), x(i+1), …, x(i+m-1)],i=1,2,…,N-m+1. Vector length m research signal variability or complexity has been correlated is known as the embedded dimension. These vectors represent m with physiological conditions. Direct assessment of signals consecutive values of the signal, commencing with the i-th point. complexity/variability thus offers certain advantages in II. Calculate the distance between X(i) and X(j), clinical research. d=[X(i), X(j)], as the maximum absolute difference between Electrohysterogram (EHG) is noninvasive method for retheir respective scalar components, where k=0, …, m-1. cording electrical activity of uterine muscles from the abdominal surface. Through the years different research d X (i ), X ( j ) = max x(i + k ) − x( j + k ) . (2) groups used different processing of uterine EHG signals. k =1, 2 ,…, m Amplitude distribution, power spectrum and its mean and/or median frequency of the uterine activity during the contrac- III. The probability of finding a vector X(j) within the distance r ·SD from the template vector X(i) is estimated by: tion was calculated [1]. The analysis, of contraction segments or bursts were made. In analysis the power density N − m +1 1 spectrum and peak frequency of the power density spectrum C rm (i ) = Θ(r ⋅ SD − d X (i ), X ( j ) ). (3) were used [3]. Possible nonlinear nature of the contraction N − m + 1 j =1 segments or bursts was tested with different methods [2]. In this report, we evaluate complexity of the uterine EHG Where Θ is the Heaviside function (Θ(z≥0)=1) and during labor by estimation of entropy. (Θ(z<0)=1), SD is the standard deviation of the given signal x(n) and r is a tolerance window. Recommended values of r are between 0.1 and 0.25 [5, 6, 7].

[

(

]

∑

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 144–147, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

)

[

]

Evaluating Uterine Electrohysterogram with Entropy

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IV. With taking the naturals logarithms of conditionals probabilities of finding a vector X(j) within the distance r ·SD from the template vector X(i) and averaging them:

φ m (r ) =

N − m +1 1 ln(C rm (i )) . ∑ N − m + 1 i =1

Brm =

(8) m

(4)

VIII. We increase the dimension to m+1 and calculate Ar (i ) :

Arm (i ) =

V. Finally the approximate entropy is defined as:

ApEn(m, r , N ) = φ m (r ) − φ m +1 (r )

1 N −m m ∑ Br (i ) . N − m i =1

(5)

This expression can be interpreted as the average of the naturals logarithms of the probabilities that vectors, which are close in m points, remain close when they are extended to m+1 points.

N −m 1 ∑ Θ(r ⋅ SD − d [X (i ), X ( j )]) (9) N − m − 1 i =1, j ≠i m

IX. Calculate Ar as:

Arm =

1 N −m m ∑ Ar (i ) N − m i =1

(10)

X. Finally the sample entropy is defined as: B. Sample entropy SampEn is the negative natural logarithm of the probability that two sequences similar for m points remain similar at the next point, where self-matches are not included in calculating the probability [10, 11, 12]. Thus, a lower value of SampEn also indicates more self-similarity in the time series. SampEn is largely independent of record length and displays relative consistency under circumstances where ApEn does not [10]. In addition to eliminating self-matches, the SampEn algorithm is simpler then the ApEn algorithm, requiring one-half as much time to calculate. Formally, given N data points from a time series {x(n)}={x(1), x(2), …, x(N)}, to define SampEn, one should follow these steps: 1. Form N-m+1 vectors X(1), …, X(N-m+1) defined by X(i)=[x(i), x(i+1), …, x(i+m-1)], for 1 ≤ i ≤ N-m+1. Those vectors represent m consecutive values of the signal, commencing with the i-th point. Calculate the distance between X(i) and X(j), d=[X(i), X(j)], as the maximum absolute difference between their respective scalar components:

d [ X (i ), X ( j )] = max ( x(i + k ) − x( j + k ) ). k =1, 2 ,…, m

(6)

VI. For a given X(i), count the number of j (1 ≤ j ≤ N-m, i ≠ j), such that the distance between X(i) and X(j) is less than or equal to r·SD:

Brm (i ) =

N −m 1 ∑ Θ(r ⋅ SD − d [X (i ), X ( j )]) (7) N − m − 1 j =1, j ≠i m

VII. Calculate Br as:

⎛ Am SampEn(m, r , N ) = − ln⎜⎜ rm ⎝ Br

⎞ ⎟⎟ . ⎠

(11)

C. Uterine EHG Uterine EHG measuring protocol is described in details in article Electromyographic observations on human cervix during labor [14], therefore just basic features are given. In this report we studied three different labors, which were randomly chosen from data base [14]. EHG activity of uterus was detected using three surface skin Ag-AgCl disc electrodes. It was measured in two directions longitudinally and transversely. The sampling frequency was 18,2 Hz. The majority of uterine EHG activity lies in between 0.1 Hz and 3 Hz [16]. Therefore digital filtering (Butterworth) was applied. Every frequency outside the mentioned window is treated as a noise. To calculate ApEn and SampEn on appropriate number of points we decrees the sampling rate by keeping every second sample starting with the first sample. ApEn and SampEn were calculated on 4500 data points, therefore their values are available every 8.2 minutes All data processing was made in Matlab. III. RESULTS SampEn values calculated from uterine EHG during three different labors were in general lover then the values that we gain with ApEn. Also the values that we gain with ApEn were much more spread out then the values that we gain with SampEn. We came to conclusion, as it was already written in literature [10, 12], that SampEn gives better results. The results that are described later on are gain with SampEn.

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Fig. 1 Values of SampEn during the labor. The zone in the middle represents border between latent (on the left side of zone) and active (on the right side of zone) phase

J. Vrhovec, A. Macek Lebar, D. Rudel

Fig. 2 Values of SampEn during the active phase of the labor

A. Case 1; normal labor: latent and active phase In figure 1 the values of SampEn during the labor are shown. The border between the latent end the active phase of the labor was determined by obstetrician as 4 centimeters cervical dilatation. The border is marked with the zone in the middle of the figure 1. On x-axes is time in minutes, while on y-axes are the SampEn values. The complexity of the EHG signal is changed with time. In the latent phase the values of SampEn are higher and more spread out then later on in the active phase. The lower are the SampEn values, less complex is the signal. The SampEn values in active phase are visibly reduced in the course of time. The signal in this phase becomes more foresee. B. Case 2; normal labor: active phase In figure 2 SampEn calculated from uterine EHG of active phase of normal labor is shown. On x-axes is time in minutes, while on y-axes are the values of sample entropy. The complexity of the EHG signal is visibly reduced in the course of time. C. Case 3; labor: influence of oxytocin on entropy In figure 3 the values of SampEn during the labor which ended with C section are shown. On x-axes is time in minutes, while on y-axes are the values of SampEn. The border between the latent and the active phase of the labor was determined by obstetrician as 4 centimeters cervical dilatation. The border is marked with the zone in the middle of

Fig. 3 Values of SampEn during the labor. The zone in the middle separates the latent (on the left side of zone) and the active (on the right side of zone) phase. The lines mark the time, when the specific dose of oxyitocin was beginning to be given to the patient. The given dose is written on the line the figure 3. Oxytocin is the most freguently used ecbolic agent. Oxytocin activates process, that result in contraction of uterine muscles. The dose and the time, when oxyitocin was given to the patient, is marked in figure 3 with the line and the dose given at that time is written on the line. Oxytocin has caused more predictable contractions of uterine muscles and consequently the values of SampEn are visibly reduced in the course of time after oxytocin was given.

CONCLUSIONS Entropy, as a measure of complexity/variability of uterine EHG signals, indicates the course of labor. Lower

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values of sample entropy correspond to reduced complexity. The complexity of uterine EHG activity during normal labor is reduced in the course of time. Results of our study show that in the latent phase the values of SampEn are usually over 0.1 and more spread out. When the active phase is reached the trend of SampEn values becomes reasonable and the values of SampEn drop under 0.1. The oxytocin has an influence on activities, which affect the values of SampEn. SampEn values are reduced after oxytocin dose.

147 7. 8. 9.

10.

ACKNOWLEDGMENT The study was supported by Slovenian Research Agency and Ministry of Higher Education, Science and Technology.

REFERENCES 1. 2. 3. 4. 5. 6.

Iams J.D et all, (2002), Frequency of uterine contractions and the risk of spontaneous preterm delivery, N Engl J Med, Vol. 346, pp. 250 - 255 Radhakrishnan N et all, (2000), Testing for nonlinearity of the contraction segments in uterine electromyography, Int. J. Bifurcat. Chaos, Vol. 10, pp. 2785 – 2790 Doret M etall, (2005), Uterine Electromyography Characteristic for Early Diagnosis of Mifepristone-Induced Preterm Labor, The Am. J. Obstet. Gynecol., Vol. 105, pp. 822-830 Garfield RE et all, (2002),Uterine Electromyography and Light-Induced Fluorescence in the Management of Term and Preterm Labor, J Soc Gynecol Investig, Vol 9, pp. 265-275 Pincus SM (1993), Heart rate control in normal and abortedSIDS infants,” Am J Physiol., Vol.264, pp. R638-46 Lake DE et all, (2002) Sample entropy analyses of neonatal heart rate variability, Am J Physiol., Vol 283, pp. R789-R797

11. 12. 13. 14. 15. 16.

Pincus SM (1991) Approximate entropy as a measure of system complexity,” Proc. Natl. Acad. Sci. USA, Vol. 88 pp. 2297–2301 Rezek I.A et all, (1998) Stochastic Complexity Measures for Phsiological Signal Analysis, IEEE Trans. on BME, Vol.45(9), pp1186-1191 Hornero R et all, (2005) Interpretation of Aproximate Entropy: Analysis of Intracranial Pressure Approximate Entropy During Acute Intracranial Hypertension, IEEE Trans. on BME, Vol.52, pp1671-1680 Richman JS et all, (2000) Physiological time-series analysis using approximate entropy and sample entropy, Am J Physiol Heart Circ Physiol, vol.278, pp. H2039-H2049 Abasolo D. et all, (2006) Entropy analysis of the EEG background activity in Alzheimer’s disease patients, Physiol. Meas., Vol. 27, pp 241-253 Govindan RG et all, (2006) Revisiting sample entropy analysis at www.elsevier.com/locate/physa. Rezek IA et all, (1998), Stochastic Complexity Measures for Phsiological Signal Analysis, IEEE Trans. on BME, Vol. 45 Pajntar M, et all, (1987),Electromyographic observations on the human cervix during labor, Am. J. Obstet. Gynecol, Vol. 156, pp. 691-697 Leman H et all, (1999), Use of the Electrohysterogram Signal for Characterization of Contractions During Pregnancy, IEEE Trans. on BME, Vol.46, pp1222-1229 Jezewski J. et all, (2005), Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterous as an alternative to mechanical approach, Phisiol. Meas.,Vol26, pp 753-767 Author: Institute: Street: City: Country: Email:

Jerneja Vrhovec Faculty of Electrical Engineering Trzaska 25 Ljubljana Slovenija [email protected]

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Evaluation of adaptive filtering methods on a 16 electrode electrohysterogram recorded externally in labor J. Terrien1, C. Marque2, T. Steingrimsdottir3 and B. Karlsson1 1 2

Reykjavik University/Department of biomedical engineering, Reykjavik, Iceland UTC/Department of biomechanics and biomedical engineering, Compiegne, France 3 Landspitali University hospital/Dept. ob-gyn, Reykjavik, Iceland

Abstract— Mapping of uterine contractions by recording the electrohysterogram (EHG) in many places on the abdominal wall is a new way of investigating the electrical activity of the uterus. Good spatial resolution is an important issue for this technique to provide new information. The use of monopolar recordings is an obvious way of increasing resolution, in spite of their having a lower signal to noise ratio (SNR) than bipolar measurements. We explored the use of the LMS and RLS filter as well as Laplacian filtering for the increase in monopolar EHG SNR. The best results for monopolar signals were obtained using the RLS algorithm but the SNR is still lower than that obtained on bipolar signals. The resulting EHG signals are nevertheless sufficiently improved to clearly identify EHG bursts during contractions. A precise selection of the different methods parameters, as well as an increase in the number of studied contractions, has to be done in order to confirm these preliminary results. Keywords— EHG, labor, uterus, monopolar recording, adaptive filtering

I. INTRODUCTION The electrical activity of the uterus or electrohysterogram (EHG) has been shown to have a predictive value for labor [1, 2]. This work has almost exclusively used localized measurements of EHG and focused on the higher frequency content of the EHG signal, often thought to be associated with the excitability of uterine cells. The propagation of the EHG and thus the good synchronization of the whole organ is also an important factor in contraction efficiency. But little effort has been put in to investigate this aspect of uterine contractions. To investigate the propagation of contractions it is natural and obvious to measure EHG on a grid of electrodes placed on the expectant mother’s abdomen. In order to obtain a precise mapping of the uterine electrical field during contractions, a high spatial resolution is needed. The total number of electrodes is however limited by the abdominal surface. We have made measurements using a 4x4 electrode grid and in this paper describe how we propose to improve spatial resolution of the measurements in the presence of considerable noise.

The analysis and characterization of electrophysiological signals is often difficult due to their usually low signal to noise ratio (SNR). It is all the more so since we are dealing with low amplitude phenomena and/or deep potential sources. As a result, electrophysiological signal are usually recorded in a bipolar manner, meaning that two electrodes are placed fairly closed together and the potential difference between them recorded. The resulting signal contains information on the variation in potential at both locations. In our application, the spatial resolution is important and the number of electrodes is limited. The use of monopolar recordings (potential measured compared to a remote and/or electrically inert patient ground) improves spatial resolution at the cost of a much reduced SNR. It is then necessary to correctly filter these signals without loss of pertinent information. The type of filter or filtering methods which are appropriate are highly dependent on the noise and signal characteristics. The worst situations are encountered when we have overlapping spectra or none stationary noises. In these situations classical linear filters can not be used. Specific filtering methods, such as wavelet and adaptive filtering, have been developed for these situations. The EHG is a noisy signal even recorded by bipolar electrodes [3]. The typical noises in the EHG are maternal and fetal electrocardiograms (ECG), abdominal muscle electromyogram (EMG), maternal and fetal movement artifacts and electronic noises from the surrounding electronic devices. Wavelet filtering has been used successfully on bipolar EHG for removing maternal and fetal ECG as well as stationary electric noises [3]. However, wavelet filtering assumes that noises are of low amplitude when compared to the signal of interest. The noises in monopolar EHG are not stationary and usually of high amplitude and thus can not be rejected by classical filters or wavelet filtering. This has lead us to the conclusion that using adaptive filter on monopolar EHG may be appropriate and advantageous. In this paper, we explore the possibility of using adaptive filter in order to obtain high EHG SNR in signals recorded externally on women during labor. We used two well known adaptive filter algorithms (LMS and RLS) and calculated their performance for various filter orders. The

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SNR for the two filters obtained were then compared with those obtained by bipolar recording and a Laplacian filter. II. MATERIALS AND METHODS A. Instrumentation and experimental protocol The measurements were performed by using a 16channel multi-purpose physiological signal recorder most commonly used for investigating sleep disorders (Embla A10). Reusable Ag/AgCl electrodes were used (interelectrode distance: 2.1 cm). The measurements were performed at the Landspitali University hospital in Iceland using a protocol approved by the relevant ethical committee (VSN 02-0006-V2). The subjects were healthy women in the first stages of labor having uneventful singleton pregnancies. After obtaining informed consent, the skin was carefully prepared using an abrasive paste and alcoholic solution. After that, the sixteen electrodes were placed on the abdominal wall according to Fig. 1. The third electrode column was always put on the uterine median axis and the 10-11th electrode pair on the middle of the uterus (fundus to symphysis). Reference electrodes were placed on each hip of the woman. The signal sampling rate was 200 Hz. The recording device has an anti-aliasing filter with a high cutoff frequency of 100 Hz. The tocodynamometer paper trace was digitalized in order to facilitate the segmentation of the contractions. In this preliminary study, two women in spontaneous labor were enrolled at 37 and 39 WG. The recording duration was approximately 1 hour in both cases. We have clearly identified 11 and 6 contractions on these recordings. All the EHG bursts were segmented manually with the help of the tocodynamometer trace. 1

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• An adjustable coefficient numerical filter • An algorithm of coefficient modification based on an optimization criterion The reference signal, n(t), the filter output, y(t), as well as the desired signals, d(t), are used in the algorithm of modification of the filter coefficients. The coefficient adaptation is performed so as to make the output of the filter converge toward the desired signal. The output of the signal is given by y(t) = n(t) x W(t), where W(t) are the filter coefficients at time t. The optimization is made in order to minimize the error e(t) = d(t) – y(t) in accordance to the chosen adaptation algorithm. It has to be noticed that a certain iteration number is necessary for the filter to converge to the optimal solution or steady state. Several adaptation algorithms exist. The most popular ones are LMS (Least Mean Square) and RLS (Recurrent Least Square) algorithms. LMS and RLS algorithms are both stochastic-gradient algorithm. A precise description of the mathematical formulation and numerical implementation of these filters is described in [4]. The final recursive formulas of the LMS and RLS algorithm are: LMS : Wi = W i −1 + μ d i* [n ( i ) − d i W i −1 ] i ≥ 0 , W −1 = initial guess RLS : ⎡ λ −1 Pi −1 d i* d i Pi −1 ⎤ Pi = λ −1 ⎢ Pi −1 − ⎥ 1 + λ −1 d i Pi −1 d i* ⎦ ⎣ W i = W i −1 + Pi d i* [n ( i ) − d i W i −1 ], i ≥ 0 −1

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Where ε is a regularization factor, λ a forgetting factor and μ a step size. Laplacian fiter: If we considere the signal matrix Si,j, the Laplacian filtering is performed as follow: Si,j = Si,j – ¼ (Si+1,j + Si-1,j + Si,j+1 + Si,j-1). On the border of the signal matrix, we used only the available signals usually used in the Laplacian. For the monopolar signal #2, we used only the signals 1, 3 and 6 for example. The Laplacian filter was used in order to understand if the noise, present on monopolar signal, is identical on each channel surrounding the channel of interest. Under the

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Evaluation of adaptive filtering methods on a 16 electrode electrohysterogram recorded externally in labor

we obtained more and more divergences (lower SNR than the initial SNR) of the LMS filter with the increase in the filter order. The RLS filter seems to be a better algorithm with a better stability with respect to the filter order. 0.4 0.2 0 -0.2

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hypothesis that EHG is a stochastic zero mean signal, the mean of the surrounding channels corresponds only to nonpropagating common noises. If it is the case, the results of the Laplacian (substracting this common noise to the signal) and of the adaptive filtering should be the same. The computation of the Laplacian filter is however less time consuming and could be of interest for routine processing. Comparison of methods: In this work we try to define the best filter order. The parameters ε, λ and μ were considered to be constant. We used ε = 0.001, λ = 1 and µ = 0.1. The initial guess of filter coefficients was always W-1 = 0. The reference signal of the adaptive filters was define as the mean EMG of the four vertical monopolar EMG of the current EMG channel, signals used for the Laplacian filtering. In order to obtain the best filter order, we evaluate the resulting median SNR calculated on each contraction with the filter lengths {4; 8; 12; 16; 20; 24; 28; 32; 36; 40; 48; 56}. For each contraction and each EMG channel, the SNR was estimated by computing the energy of the base lines present before and after the EHG burst, and the energy of the burst. The obtained SNR were compared with those obtained with bipolar signal (vertical differentiation, Fig. 1) and after Laplacian filtering. In an other way, the adaptive filter could take into account any transformations (delay, attenuation, ...) of the non-propagating noises from one channel to an other which is not possible to do by using Laplacian. For statistical comparison, we used the sign test with a minimal significant level of 0.05.

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The monopolar signals present a poor SNR (median SNR = -3.98 dB). The main noises are high frequency electronic noise and probably electronic impulses from the injection pump (Fig. 2). The vertical differentiation or bipolar signals present a higher SNR (median SNR = 7.78 dB). The signals obtained by horizontal differentiation present practically the same SNR (7.5 dB). The Laplacian filter gives positive SNR but lower than those of bipolar signals (median SNR = 2.97 dB). The median SNR of bipolar signals and those obtained after Laplacian filtering are presented Fig. 4. There is a significant difference of 1% between the two methods. The results of the adaptive filters, in function of the filter order, are presented Fig. 3. A significant difference was obtained for all filter lengths. The RLS filter gives higher SNR than the LMS method (p ≤ 0.01). The RLS filter is less sensitive to the filter order than LMS filter. The highest SNR was obtained for a RLS filter of order 4. A decrease in the median SNR with the increase in the order of the LMS filter was noticed. The analysis of the results showed that

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The comparison of the median SNR obtained by RLS filtering, bipolar differentiation and Laplacian filtering is presented Fig. 4. The RLS SNR are significantly lower than those obtained by bipolar differentiation (p ≤ 0.01). There is no statistical difference between RLS filtering results and Laplacian filtering ones (p = .14). The RLS algorithm seems however to give qualitatively higher SNR than Laplacian filter. An example of the result of the different methods is presented Fig. 2. IV. DISCUSSION The EHG is a noisy electrophysiological signal even recorded in a bipolar configuration. Monopolar EHG recording are usually used internally were SNR is much better, like for example described by [5, 6]. We have explored the possibility of using adaptive filters in order to obtain high SNR for monopolar EHG. The use of adaptive filters has the advantages of dealing with non-stationary noises of possibly high amplitude. First, we compared the well known LMS and RLS adaptive filters. The RLS algorithm seems to be the best method when applied on monopolar EHG. This method gives higher SNR and is less sensitive to the filter order. Several occurrences of divergence have been observed with LMS filters of high order. The RLS SNR are not significantly different from those obtained with Laplacian filtering. The SNR obtained with RLS and Laplacian filter are however significantly lower than bipolar ones. The difference between adaptive and Laplacian filtering could be explained by the fact that the Laplacian filtering always removes the signal obtained by the average signal of surrounding channels as opposed to adaptive filters that remove only the parts of the original signal that correlate to the reference signal. The noises present on each channel are therefore not always the same but have a similar characteristic form locally. Adaptive filter can take into account such local transformation of a global (spatially average) noise. Adaptive filtering by RLS algorithm, in our case, is therefore the best of the tested method when applied to monopolar EHG. A part of noises can also be stochastic and then more cancelled by the averaging procedure of the Laplacian filtering as well as by the definition of the reference signal of adaptive filter when compared to vertical differentiation (bipolar signals). A bad estimation of common mode noise and non-zero mean noise can also explain the lower SNR of Laplacian and adaptive filter. Our results have been obtained with constant filter parameters except for the filter order. The selection of the best filter parameters remains to be done in a manner similar to the one used to select the filter order. We have not

studied the filter convergence to a steady-state but the segmentation of EHG burst, with a long baseline before it, insures a convergence of the filter during the electrical burst. We saw that the results vary highly with the contraction considered imposing the use of non-parametric statistical test due to non-gaussian distributions of SNR values. An increase of the number of analyzed segments can give more relevant results. The evaluation of more sophisticated adaptive filter can also give higher SNR. A better definition of the reference signal can also be a key for the increase of the results quality. V. CONCLUSIONS In this paper, we explored the use of adaptive filters for the pre-processing of monopolar EHG recorded externally in woman. We investigated the performances of the LMS and RLS algorithms. The RLS filtering algorithm gives higher SNR than LMS or Laplacian ones. In spite of a significantly lower median SNR obtained with RLS filter, as compared to the bipolar differentiation ones, the results are good enough to clearly identified EHG burst on each recording monopolar channel. An increase of the number of studied contractions has to be done in order to confirm these preliminary results. The use of other adaptive filtering algorithms, as well as an optimization of all filter parameters which have been used, can further increase the obtained EHG SNR.

REFERENCES 1. 2. 3. 4. 5. 6.

Garfield, R.E., et al., Uterine electromyography and light-induced fluorescence in the management of term and preterm labor. J Soc Gynecol Investig, 2002. 9(5): p. 265-75. Maner, W.L., et al., Predicting Term and Preterm Delivery With Transabdominal Uterine Electromyography. Obstet Gynecol, 2003. 101(6): p. 1254-1260. Leman, H. and C. Marque, Rejection of the maternal electrocardiogram in the electrohysterogram signal. IEEE Trans Biomed Eng, 2000. 47(8): p. 1010-7. Sayed, A.H., Fundamentals of adaptive filtering. 2003, Hoboken, New Jersey: John Wiley& Sons. Duchêne, J., C. Marque, and S. Planque. Uterine EMG signal : Propagation analysis. in Annual International Conference of the IEEE EMBS. 1990. Lammers, W.J., Circulating excitations and re-entry in the pregnant uterus. Pflugers Arch, 1997. 433(3): p. 287-93. Author: Jeremy Terrien Institute: Street: City: Country: Email:

School of Science and Engineering, R.U. Kringlan 1 103 Reykjavik Iceland [email protected]

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Predictive value of EMG basal activity in the cervix at initiation of delivery in humans D. Rudel1, G. Vidmar2, B. Leskosek2 and I. Verdenik3 1

MKS Electronic Systems Ltd., Ljubljana, Slovenia University of Ljubljana, Faculty of Medicine, Institute of Biomedical Informatics, Slovenia 3 University Medical Centre, Department of Obstetrics and Gynecology, Ljubljana, Slovenia

2

Abstract— We present efforts to objectively assess cervical ripeness in humans. The hypothesis was that the cervical EMG basal activity might reflect readiness of the cervix for delivery. 47 women at initiation of delivery were involved in the study. EMG parameters – amplitude (URMSA) and frequency contents (MFA) – were related to Cumulative Bishop Score values (CBS) assessed by an obstetrician in each woman at the labor onset. The results show that the parameters are predictive of the CBS, both correlating negatively with the CBS value. Hence, EMG parameters have potentials to become objective indicators in assessment of cervical ripeness in humans. This would empower an obstetrician in his/her decisions how to further conduct the labor. Keyword — EMG, cervix, labor, Bishop Score, ripeness

I. INTRODUCTION Recent research on the human cervix [1,2,3,4,5,6] and the animal cervix [7,8,9,10] has recognized that the cervix is an active organ that plays an important role in the process of pregnancy and labor. The cervix prepares itself for labor during the process of ripening [11,12]. Failure of the cervix to ripen at term may be followed by failure to progress in labor. At labor, before a regimen of conducting the labor is selected, the obstetrician assesses cervical ripening or preparedness for induction. In the absence of a ripe (favorable) cervix, steps are to be taken for its preparation. The Bishop scoring system [13] is used in many delivery rooms for quantifying and scoring cervical ripeness. It is based on the assessment of cervical physical properties. Attributes that advocate for cervical ripeness are an effaced, dilated, favorable cervix with its canal os directed as much forward as possible. Each of the attributes is scored, whereby the sum of scores ranges from 0 to 13. Scores for unripe cervices range from 3 to 6, and for ripe cervices from 7 to 12. For decades, methods to quantitatively evaluate ripeness throughout labor have been sought. Different non-invasive and invasive technical solutions have been proposed, among them a combination of EMG methods, dilatation and IUP in

humans [3] and cows [9]. In this paper, an attempt is described to predict cervical ripeness from EMG only. It has been proved that in humans [1,2,3,4,5,14,15,16] and in animals [7,8,9,10] EMG signals derived from the cervix reflect electrical activity of smooth muscle cells in the cervix. The activity is different in women at different stages of the cervical ripeness at the onset of labor [17]. When labor progresses and the cervix ripens, EMG activity changes in its pattern and the EMG content thus probably (at least partly) reflects changes in the status of cervical ripeness [1,3,4,5,17]. The importance of EMG basal activity registered in the cervix at the onset of labor was therefore hypothesized to potentially reflect the level of cervical readiness for successful labor and thus the level of its ripeness. The EMG basal activity of the smooth muscle tissue in the cervix is defined as EMG activity registered in the periods when there are no locally produced bursts in EMG activity [4,5] and the contribution of uterine corpus EMG activity is minimized (i.e., there are no uterine contractions). The paper is aimed at relating Bishop Score as being a measure of cervical ripeness to EMG parameters calculated from the EMG basal activity signal, in order to see if the EMG parameters have any predictive value for cervical ripeness. II. METHODS A. Study population Forty-seven primiparous healthy women at term undergoing induction of labor with amniotomy and subsequent oxytocin infusion were included in the study of cervical electromyographic activity (EMG). EMG and intrauterine pressure were registered electronically throughout the whole latent and active phase of labor without major artifacts. Clinical data for each labor (Bishop Score, duration of the latent phase) were collected from the patient's labor documentation. The National Medical Ethics Committee approved the study and informed consent was obtained from each woman before being enrolled in the study.

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B. EMG and IUP measurements procedure Patient preparation and EMG and IUP measurements procedure are described in details elsewhere [4]. To record the EMG activity of smooth muscle tissue at the exterior wall of the cervix, two fetal spiral steel electrodes (Hewlett Packard 15130A) were inserted directly into the cervical tissue from the vaginal side in the outer aspect of the cervix, circumferential to the cervical canal [1]. A reference electrode was attached to the woman's thigh. EMG signals were amplified (differential amplifier A=2000) and filtered (0,03 Hz – 5 Hz). Intrauterine pressure (IUP) was measured to identify uterine contractions. EMG and IUP signals were registered on the monitor chart recorder and digitally stored (12-bit A/D conversion, 20 Hz sampling rate). The registration of EMG and IUP began approximately 10 minutes after amniotomy. C. EMG signal processing and analysis Each EMG record of the selected labors was peer reviewed for the quality of the recording. One 20-minute measurement interval was selected from each labor record containing no major artifacts in EMG. The interval was to start as close to the onset of labor as possible. Within the selected 20-minute intervals, periods with no bursts in EMG activity were visually selected for further processing. Selected periods classified as basal EMG activity ranged from 3.3 to 13.0 minutes in total duration. The selected EMG recordings were filtered digitally (Butterworth band pass filter 0.3 Hz - 3.0 Hz) and processed in time and frequency domain. EMG signals were visually assessed to identify characteristic EMG patterns at different stages of cervical ripeness. The Root Mean Square of the EMG signal voltage (URMS) and median frequency (MF) of the EMG signal were calculated for each selected interval, their average values (URMSA, MFA) determined and Power Spectrum Density (PSD) plotted. For the statistical analyses, the following parameters were used: cumulative Bishop Score (CBS), values of Bishop Score components (see Section III.B), the results of EMG signal processing (URMSA and MFA), time to delivery (in minutes) and number of contractions (during the selected 20-minute interval). The ability of EMG characteristics to predict the CBS, time to delivery and number of contractions was tested using weighted multiple linear regression (WLS) with URMSA and MFA as the independent variables and cases weighted by total duration of the selected EMG intervals without bursts. The statistically significant association among CBS, URMSA and MFA was visualized using 3D-scatterplot with local regression smoother (with Epanechnikov kernel).

D. Rudel, G. Vidmar, B. Leskosek and I. Verdenik

Exact binomial logistic regression was used for testing the association of EMG characteristics with individual components of the Bishop score, which were dichotomized for the purpose. Statistical analyses were performed using SPSS for Windows 14.0.2 (SPSS Inc., Chicago, IL, 2004) and Cytel Studio 7.0.0 (Cytel Software Corp., MA, 2005). III. RESULTS A. Results of visual assessment EMG signals of four selected labors are presented (Fig. 1) as exhibiting typical patterns of EMG activity during ripening process. The EMG record of an unripe cervix (Fig. 1, top row) is dense (MFA > 1 Hz) with hindered EMG bursts recorded at uterine corpus mechanical contractions. With progression of ripening, EMG pattern changes in the periods between EMG bursts. The EMG signal amplitude in those periods becomes lower and less dense and the EMG bursts at contractions more outstanding (Fig. 1, middle two rows). The process of reduction of EMG activity between uterine corpus mechanical contractions progresses until there is almost no EMG activity between EMG bursts between consecutive uterine contractions (Fig. 1, bottom row). Changes in EMG signal frequency contents are adequately reflected in corresponding PSD as presented in Figure 2. The EMG of an unripe cervix (Figure 2a) has three frequency groups: one around 2.4 Hz, the other around 1.2 HZ and the third below 1 Hz. With ripening of the cer-

Fig. 1: EMG as derived from the cervix at an initiation of a delivery in 4 women at different stage of the cervical ripeness: unripe cervix (phase 1), partially ripe cervix (phase 2 and 3) and ripe cervix (phase 4). The EMG amplitude scale differs between phases.

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Fig. 2: Power Spectrum Density charts corresponding to the EMG signals from Figure 1: a) unripe cervix; b) ripening cervix; c) ripening cervix; d) ripe cervix. Solid line represents PSD during EMG bursts, dashed line between bursts. Note that a) has a different horizontal scale, and d) has a different vertical scale.

vix, the first group diminishes, the second is still well expressed and the first increases in its power contents (Figure 2b and 2c). In a ripe cervix, only the first group of frequencies below 1 Hz is present (Figure 2d). B. Results of statistical analysis Statistical analysis showed that the average EMG amplitude URMSA and average median frequency MFA as the characteristic parameters of the selected intervals of the EMG basal activity are predictive of the cumulative Bishop Score CBS (p=0.017 for the model; adjusted R2=0.131). As illustrated in Fig. 3, both URMSA and MFA are negatively associated with Bishop Score (β =-0.364, p=0.013 for URMSA; and β =-0.045, p=0.045, for MFA). The cumulative Bishop Score value is high when both URMSA and MFA have low values and decreases when with increasing URMSA and/or MFA. No statistically significant association could be found among URMSA and MFA on one hand, and the individual Bishop Score components (p-values are for the model from LR test): cervical channel dilatation DILA, dichotomized as 2-3 vs. 0-1: p=0.104; cervical effacement EFFA, 2-3 vs. 01: p=0.105; and cervical consistency CONS, 2 vs. 0-1: p=0.311. Similarly, no statistically significant association was found with time to delivery (p=0.816 for the model), or with number of contractions (p=0.475 for the model). IV. DISCUSSION The focus of our study was on the cervical EMG basal activity. Statistical analysis confirms the observations resulting from Fig. 1 and Fig. 2. As seen from Fig. 3, the

Fig. 3: Association of EMG characteristic parameters URMSA and MFA with Cumulative Bishop Score. Local regression smoother is superimposed on the point-cloud. cervical EMG basal activity parameters (URMSA, MFA) relate to the Bishop cumulative value. At the onset of an induced labor, the average cervical EMG signal amplitude (URMSA) and the average median frequency (MFA) are negatively associated with the cumulative Bishop Score. Consequently, an obstetrician may expect a low cumulative Bishop Score value for the labor when EMG signal is of high amplitude (e.g., URMSA > 50 µV) and/or having high median frequency value (e.g., MFA >> 1 Hz). When presented on a monitor with a standardized scale, the EMG signal would be of high amplitude and of a dense trace. Conversely, high cumulative Bishop Score value is expected when both the EMG amplitude and the EMG median frequency have low values (e.g., URMSA < 25 µV and MFA < 0.5 Hz). In that case, the EMG signal would be of low amplitude and its polarity would change slowly. The results are in line with our previous findings [4,5,6,17]. V. CONCLUSIONS At the onset of an induced labor, EMG activity registered in the periods when there are no uterine contractions and no bursts in the cervical EMG signal is considered the EMG basal activity of the cervix. Its average amplitude (URMSA) and average median frequency (MFA) are negatively associated with the cumulative Bishop Score as assessed by an obstetrician at a digital check of the cervical (un)ripeness at the beginning of the labor. High URMSA and high MFA advo-

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cate for low Bishop Score values, while low URMSA and low MFA advocate for high Bishop Score values. It may be concluded that to a certain extent, EMG signal parameters URMSA and MFA reflect the stage (level, status) of cervical ripeness and thus the readiness of the cervix for successful labor. Hence, an adequately processed cervical EMG signal, when visually presented to an obstetrician in a delivery room, could help him/her to better assess cervical ripeness at the onset of labor, thus supporting him/her in deciding how to proceed in conducting the labor.

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ACKNOWLEDGMENT The study was supported by the Ministry of Science and technology of the Republic of Slovenia (grants L3-7365, J38759, J3-5342, J3-2361). The authors express their gratitude to Mr. Darko Oberzan, MKS Ltd. Ljubljana, for his help in EMG signal processing.

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Pajntar M, Roskar E, Rudel D. (1987) Electromyographyc observations on the human cervix during labor. Am J Obstet Gynecol 156(3): 691-697 Pajntar M. (1994) The Smooth Muscles of the Cervix in Labor. Eur J Obstet Gynecol Reprod Biol 55: 9-12 Olah KS. (1994) Changes in cervical electromyographic activity and their correlation with the cervical response to myometrial activity during labor. Europ J Obstet & Gynaecol Rprod Biol 57:157-9 Rudel D, Pajntar M. (1999) Active Contractions of the Cervix in the Latent Phase of Labor. Br J Obstet Gynaecol, 106: 446-52 Rudel D, Pajntar M. (1999) Contractions of the Cervix in the Latent Phase of Labor. Contemp Reviews in Obstet Gynaecol 11(4):271-9 Pajntar M, Leskosek B, Rudel D, Verdenik I. (2001) Contribution of cervical smooth muscle activity to the duration of latent and active phases of labor. Br J Obstet Gynaecol 108:1-6 Toutain PL, Garcia-Villar R, Hanzen C, Ruckebusch Y. (1983): Electrical and mehanical activity of the cervix in the ewe during pregnancy and parturition. J Reprod Fertil 68: 195-204.

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16.

17.

Garcia-Villar R, Toutain PL, Ruckebusch Y. (1984) Pattern of electrical activity of the ovine uterus and cervix from mating to parturition. J Reprod Fertil 72:143-52 Breeveld-Dwarkasing VN, Struijk PC, Lotgering FK, Eijskoot F, Kindahl H, van der Weijden GC, Taverne MA. (2003) Cervical dilatation related to uterine electromyographic activity and endocrinological changes during prostaglandin F(2alpha)induced parturition in cows. Biol Reprod. 68(2):536-42 Cavaco-Goncalves S, Marques CC, Horta AE, Figueroa JP. (2006) Increased cervical electrical activity during oestrus in progestagen treated ewes: Possible role in sperm transport. Anim Reprod Sci. 93(3-4):360-5 Uldbjerg N, Ulmsten U, Ekman G. (1983) The ripening of the human uterine cervix in terms of connective tissue biochemistry. In: Pitkin RM, Scott JR, Ulmsten U, Ueland K eds. Clin Obstet Gynecol No.1, Vol. 26. Philadelphia: Harper & Raw 14-26 Garfield RE, Saade G, Buhimschi C, Buhimschi I, Shi L, Shi SQ, Chwalisz K. (1998) Control and assessment of the uterus and cervix during pregnancy and labor. Hum Reprod Update 4(5):673-95 Bishop EH. (1964) Pelvic scoring for elective induction. Obstet Gynecol 24: 226 Serr DM, Porath-Furedi A, Rabau E, Zakunt H, Mannor S. (1968) Recording of electrical activity from the human cervix. J Obstet Gynaecol Br Cmwlth 75: 360-3 Hofmeister JF, Slocumb JC, Kottmann LM, Picciottino JB, Ellis DG. (1994) A Noninvasive Method for Recording the Electrical Activity of the Human Uteus in Vivo. Biomed Instrum Technol 28: 391-404 Leskosek B, Pajntar M, Rudel D. (1998) Time/frequency analysis of the uterine EMG in pregnancy and parturition in sheep. In: Magjarević R, ed. Biomedical measurement and instrumentation – BMI'98. Proc Vol 3, 8th Int IMEKO TC-13 Conf Measurement in Clinical Medicine & 12th Int Symp Biomed Eng Dubrovnik. Zagreb: KoREMA, 2003, pp 106-9 Pajntar M, Verdenik I. (1995) Electromyographic activity in cervices with very low Bishop score during labor. Int J Gynecol Obstet 49: 277-81

Drago Rudel MKS Electronic Systems Rozna dol. C.XVII/22b SI-1000 LJUBLJANA, SLOVENIA [email protected]

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Uterine Electromyography in Humans – Contractions, Labor, and Delivery R. E. Garfield and W. L. Maner University of Texas Medical Branch/Reproductive Science, Galveston, Texas, USA Abstract— Today’s maternal/fetal monitoring lacks the capability to diagnose labor and predict delivery. The objective of this work was to demonstrate that uterine electromyography (EMG) is proven to be a viable alternative to current monitoring techniques. Uterine EMG was monitored noninvasively and trans-abdominally from pregnant patients using surface electrodes. Several aspects of the uterine EMG were investigated: contraction plotting, diagnosing labor, and predicting delivery. Contractions were seen to correspond well with tocodynamometer- (TOCO-) plotted contractions. As well, increases in electrical activity were indicative of labor and imminent delivery. Uterine EMG could be a valuable tool for obstetricians if implemented on a routine basis in the clinic. Keywords— Uterus, electromyography, EMG, labor, diagnosis, prediction.

mometer (TOCO) has routinely been used in the clinic to measure contractions [13], it has been shown to have limited predictive capability [14]. Root-mean-square (RMS) signal processing has been established as a standard method for plotting signal amplitude changes [15]. Spectraltemporal mapping has been used successfully to identify spectral changes that occur in biological electrical signals [16, 17]. II. OBJECTIVES • •

I. INTRODUCTION Labor is the physiologic process by which a fetus is expelled from the uterus, and is defined loosely as regular uterine contractions accompanied by cervical effacement and dilation.[1] Preterm labor, defined as labor before 37 weeks’ gestation, is the most common obstetric complication and occurs in about 20% of pregnant women. In the United States alone, 10% of the 4 million infants born each year are premature. [2 and 3] At $1500 a day for neonatal intensive care, this constitutes a national health care expenditure well over $5 billion. [4] In addition, preterm labor accounts for 85% of infant mortality and 50% of infant neurologic disorders. Current tocolytic therapy has not decreased the rate of preterm delivery. It is argued that the failure of the current strategies to decrease the rate of preterm labor might be because once preterm labor is finally diagnosed, any therapeutic benefit is lost or temporary. Therefore, one of the keys to treating preterm labor would be early detection or prediction. What is called for is a better method of monitoring patient uterine contraction activity. Previous studies have established that the electrical activity of the myometrium is responsible for myometrial contractions [5, 6]. As well, extensive studies have been done in the last 60 years to monitor uterine contractility using the electrical activity measured from electrodes placed on the uterus [7-9]. However, more recent studies indicate that uterine EMG activity can actually be monitored accurately from the abdominal surface [10-12]. Although tocodyna-

•

To determine if uterine contraction events plotted using uterine electromyography (EMG) data, correlate with TOCO-plotted contraction events. To compare uterine electromyography of labor patients to ante partum patients. To determine whether delivery can be predicted using transabdominal uterine electromyography. III. MATERIAL AND METHODS

•

•

323 contractions vs. no-contraction events were observed from ten term-pregnant women, all of whom ultimately delivered spontaneously. Uterine EMG was measured non-invasively from the abdominal surface of each patient for 30 minutes. TOCO was used simultaneously to measure uterine contractions. The STM and RMS methods were applied to the uterine EMG data to generate contraction curves similar to TOCO “bellshaped” curves. Correspondence between the raw uterine EMG bursts and the uterine contractions plotted by the various methods was established by looking for temporal overlap of the events. Fifty patients (group 1: labor, n = 24; group 2: ante partum, n = 26) were monitored using transabdominal electrodes. Group 2 was recorded at several gestations. Uterine electrical ‘‘bursts’’ were analyzed by powerspectrum from 0.34 to 1.00 Hz. Average power density spectrum (PDS) peak frequency for each patient was plotted against gestational age, and compared between group 1 and group 2.

A total of 99 patients were grouped as either term (37 weeks or more) or preterm (less than 37 weeks). Uterine

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electrical activity was recorded for 30 minutes in clinic. EMG "bursts" were evaluated to determine the PDS. Measurement-to-delivery time was compared with the average power density spectrum's peak frequency. Receiver operating characteristic (ROC) curve analysis was performed for 48, 24, 12, and 8 hours from term delivery, and 6, 4, 2, and 1 day(s) from preterm delivery. IV. RESULTS Kappa inter-rater agreement was excellent (0.823) between EMG, TOCO, RMS and STM. Significant correlation was found between all plots. There was no significant difference in the percentage of burst/contraction events plotted by EMG, RMS, and STM compared to TOCO (Fig. 1 EMG: 114.32 ± 18.86 %; OCO: 100.00 ± 0.00 %; MS: 109.18 ± 17.05 %; STM: 102.73 ± 8.31 %). To Group 1 was significantly higher than group 2 for gestational age (39.87±1.08 vs. 32.96±4.26 weeks) and average PDS peak frequency (Fig. 2 - 0.51±0.10 vs. 0.40±0.03 Hz). The power density spectrum peak frequency increased as the measurement-to-delivery interval decreased. ROC curve analysis gave high positive and negative predictive values for both term and preterm delivery (Table 1).

Fig. 2 At term, the average PDS peak frequency was significantly higher for the 24-or-fewer-hours-to-delivery group than for the more-than-24-hours-to-delivery group, whereas at preterm, the average PDS peak frequency was significantly higher in the 4-or-fewer-days-to-delivery group than in the more-than-4-days-to-delivery group (Fig. 3).

Table 1 Labor

PPV

NPV

SENS

SPEC

GS

P

Term

.854

.889

.918

.625

1 day

< 0.01

Preterm

.857

.886

.600

.969

4 days

< 0.01

Fig. 1

Fig. 3

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V. CONCLUSIONS Uterine EMG bursts correspond strongly to TOCO contraction plots. EMG-generated contraction plots (using RMS or STM) are statistically indistinguishable from TOCO contraction plots. So, for pregnant patients exhibiting myometrial activity, uterine EMG could be used in place of TOCO in the clinic for plotting contractions. Uterine EMG in antepartum patients is significantly lower than in laboring patients delivering <24 hours from measurement, giving uterine EMG the capability to diagnose labor in the clinic. Moreover, trans-abdominal uterine EMG predicts delivery within 24 hours at term and within 4 days preterm. This methodology offers many clinical advantages and benefits to obstetricians that are not currently available to them with presently used uterine monitoring systems. Supported by NIH R01-HD037480.

REFERENCES 1. 2. 3. 4.

5. 6.

E.R. Norwitz, J. Robinson and J.R.G. Challis , The control of labor. N Engl J Med 341 (1999), pp. 660–666. U.S. Preventive Services Task ForceGuide to clinical preventive services: An assessment of the effectiveness of 169 interventions, Williams & Wilkins, Baltimore (1989). S.J. Ventura, J.A. Martin, S.C. Curtin and T.H. Matthews, Report of final natality statistics. Monthly Vital Stat Rep 45 (1997), p. 12. E.R. Brown and M. Epstein , Immediate consequences of preterm birth. In: F. Fuchs and P.G. Stubblefield, Editors, Preterm birth: Causes, prevention, and management, Macmillan Publishing, New York (1984), pp. 323–354. Marshall JM. Regulation of the activity in uterine muscle. Physiol Rev 1962; 42:213-27 Kuriyama H, Csapo A. A study of the parturient uterus with the microelectrode technique. Endocrinology 1967; 80:74853.

7. 8. 9.

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Devedeux D, Marque C, Mansour S, Germain G, Duchene J. Uterine electromyography: A critical review. Am J Obstet Gynecol 1993; 169:1636-53. Wolfs GMJA, Van Leeuwen. Electromyographic observations on the human uterus during labor. Acta Obstet Gynecol Scand Suppl 1979; 90: 1-61. Figueroa JP, Honnebier MB, Jenkins S, Nathanielsz PW. Alteration of 24-hour rhythms in the myometrial activity in the chronically catheterized pregnant rhesus monkey after 6hours shift in the light-dark cycle. Am J Obstet Gynecol 1990; 163: 648-54 Garfield RE, Buhimschi C, Control and assessment of the uterus and cervix during pregnancy and labour. Hum Reprod Update. 1998 Sep-Oct;4(5):673-95. Buhimschi C, Garfield RE. Uterine activity during pregnancy and labor assessed by imultaneous recordings from the myometrium and abdominal surface in the rat. Am.J. Obstet Gynecol 1998; 178:811-22. Garfield RE, et al, Instrumentation for the diagnosis of term and preterm labour. J. Perinat Med 1998; 26; 413-436. Newman RB. Uterine contraction assessment. Obstet Gynecol Clin North Am. 2005 Sep;32(3):341-67. Maul H, Maner WL, Olson G, Saade GR, Garfield RE. Noninvasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. Garrison LA, Lamson TC, Deutsch S, Geselowitz DB, Gaumond RP, Tarbell JM. An in-vitro investigation of prosthetic heart valve cavitation in blood. J Heart Valve Dis. 1994 Apr;3 Suppl 1:S8-22; discussion S22-4. Makfarlane P.W. A comparison of different processing techniques for measuring late potentials. Theproceedings of the international symposium on high-resolution ECG. Yokohama. Japan. July 3 - 1994 - p.136. Time domain-analysis of the signal averaged electrocardiogram: reproducibility of results. European Heart J.,1992.,V.13., Abstract suppl., p.646.

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Analyzing Distributed Medical Databases on DataMiningGrid© Vlado Stankovski1, Martin Swain2, Matevz Stimec3 and Natasa Fidler Mis3 1

Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia 2 University of Ulster, Coleraine, Northern Ireland, United Kingdom 3 University Children’s Hospital, University Medical Centre, Ljubljana, Slovenia

Abstract— Hospitals throughout Europe hold vast amounts of data in the form of patient records. Performing on-the-fly analyses of these data and their actual transformation into information and knowledge may help improve medical procedures, treatments or prevent illnesses. Grid technology has recently emerged to address the needs for efficient and effective exploitation of heterogeneous and geographically distributed resources, such as large and distributed data, open source or proprietary programs for data analysis, massive storage devices and high-performance computers. A de facto standard framework for building grid environments is the Open Grid Service Architecture (OGSA) and the corresponding Web Service Resource Framework (WSRF). The Globus Toolkit version 4, is a fully WSRF-compliant grid middleware, which addresses the needs for secure, flexible, interoperable and seamless use of grid resources. The DataMiningGrid© (www.datamininggrid.org) system was recently built on top of existing Globus technology inter alia to address the requirements of a community of medical users and enable them to perform on-the-fly analysis of geographically distributed medical databases. DataMiningGrid© is a set of grid services and user-friendly workflow editing and managing tools, which facilitate manipulation of distributed data, registering, discovery and use of grid-enabled statistical and data mining programs, their execution in the grid environment and a provenance tracking mechanism. The software is now freely available at SourceForge.net under Apache License V2. The present work illustrates the use of the DataMiningGrid© system to perform analysis of nine regional medical databases in Slovenia. Keywords— grid, distributed, database, data mining, medicine

I. INTRODUCTION Many medical studies are more relevant if conducted for a greater geographic area. Facilitating on-the-fly analyses of distributed medical data and their actual transformation into information and knowledge may help improve medical procedures, treatments or prevent illnesses. Statistical and data mining programs have become the de facto technology to address the arising medical data analysis and interpretation tasks. Because of the complexity and rising demands for distributed and heterogeneous resources, modern applications are increasingly operating in distrib-

uted computing environments over widely dispersed geographic locations. Facilitating secure and privacy-preserving data analysis in such environments requires novel techniques and systems. Grid technology [1] has recently emerged to address the needs for efficient and effective exploitation of heterogeneous and geographically distributed resources, such as large and distributed data, open source or proprietary programs for data analysis, massive storage devices and highperformance computers. A grid computing architecture facilitates the distribution of process execution and resource sharing across computational resources that are geographically widely dispersed. A de facto standard framework for building grid environments is the Open Grid Service Architecture (OGSA) [1] and the corresponding Web Service Resource Framework (WSRF). The Globus Toolkit version 4 (GT4) [2], is a fully WSRF-compliant grid middleware, which addresses the needs for secure, flexible, interoperable and seamless use of grid resources. The DataMiningGrid © 2007 system has recently been designed [3] and developed in order to meet the requirements of modern and distributed data mining scenarios. Based on the Globus Toolkit and other open technology and standards, the DataMiningGrid system provides tools and services facilitating the grid-enabling of various applications (including data mining and statistical applications) without major intervention on the application side. In fact, DataMiningGrid© is a set of grid services and workflow editing and managing tools which facilitate manipulation of distributed data, registering, discovering and use of statistical and data mining programs, their execution in the grid environment and a provenance tracking mechanism. The goal of this particular study was to address the requirements of a community of nutritionists in Slovenia by helping them perform on-the-fly analysis of geographically distributed medical databases while paying special attention to security and privacy issues. The purpose of the nutritionist study was to investigate the incidence of endemic goitre and supply with iodine in Slovenian children entering high school.

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Analyzing Distributed Medical Databases on DataMiningGrid©

II. METHODOLOGY The community of nutritionists was led by the Pediatric clinic of the University Medical Centre in Ljubljana. They defined the following scientific objectives of the study: 1. to study the prevalence of insufficient supply with iodine, which is manifested as goitre, among children entering high school in 9 regions of Slovenia, 2. to find out in which regions of Slovenia the prevalence of insufficient supply with iodine, manifested as goitre, is especially high, 3. to determine, if there is a significant difference in nutritional habits between the children with deficient supply with iodine and their peers from the control group from the same region in Slovenia, who have adequate iodine supply, and 4. to compare nutritional habits of Slovenian children entering high school with recommended Central European reference values. Approximately four thousand children were recruited in nine Slovenian regions and the data was collected in regional (relational) databases. See Fig. 1 for illustration of the procedure. The data contained, for an example, anthropometric parameters, age, sex, height, weight, residential area and residential environment, and clinical estimation of the thyroid gland size according to the criteria of the World Health Organization (WHO, UNICEF, ICCIDD). Since the data resides in several regional hospitals and the analysis of the data is to be conducted at the University Children’s Hospital in Ljubljana (University Medical Centre Regular systematic medical examination before entering high school (conducted in nine regions of Slovenia) Thyroid ultrasound, urinary iodine, TSH, thyroid hormones, thyroid auto antibodies

Urinary iodine

Nutritional counselling Anthropometrical measurements, nutritional diary and nutritional questioner Evaluation of nutrition and comparison to the nutritional recommendations Goitre + lowered concentration of urinary iodine: treatment with potassium iodine

Fig. 1 Data collection methodology

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in Ljubljana), it was decided to use the DataMiningGrid© system in parallel to their existing system. The collected data was to be analyzed by statistical and data mining approaches. In this respect, the study was not concerned with massive data and a high-performance computing approach was not needed, nevertheless, the databases existed in several schemas and were geographically distributed. According to the original procedure, data was collected in nine regions of Slovenia in parallel. Then the data were physically transferred on weekly bases to the University Medical Centre in Ljubljana for analysis. The regional researchers brought their data on a CD or a memory stick. Technologically speaking, it would have been much easier to collect the information via the Internet, but, this was considered insecure. Development of Web applications for secure upload of data should involve the use of security standards, nevertheless, in many medical scenarios, the data should actually stay where it has been collected i.e. there is an important requirement that the data should not move within the network. It is only acceptable to move summarized data or information. Key concerns of the Ethical Committee of the Medical Faculty at the University of Ljubljana for the study “Endemic goitre and supply with iodine in Slovenian children entering high school”, were the following: (a) personal data has to be de-identified for analysis, (b) identification data has to be stored in a separate database, independently accessible and secure, and (c) national and international law on ethical issues has to be followed, wherever applicable. Based on this study, we developed use-cases, which are typical for the medical domain and derived technical and none technical requirements that were then used in the design and development process for the overall DataMiningGrid© system. We also designed and developed nine regional relational databases based on MySQL technology. These databases were gradually filled in with data, while the DataMiningGrid system was used to dynamically access and query these databases and further perform data analysis. III. THE DATAMININGGRID© SYSTEM The DataMiningGrid© system was designed and developed to accommodate data mining and statistical applications from a wide range of platforms, technologies, application domains and sectors in contrast to other grid-based systems, such as Weka4WS [4] for example, which is restricted to Weka-only [5] data mining applications. It is designed around the principles of Service Oriented Architectures [3] and implements many independent grid services, each capable of carrying out a set of predefined tasks. The integration of all these services into one system with a

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Vlado Stankovski, Martin Swain, Matevz Stimec and Natasa Fidler Mis

Fig. 2 DataMiningGrid workflow for the medical study which is based on Triana Workflow Editor and Manager variety of end-user clients, results in a highly modular, reusable, interoperable, scalable and maintainable system. The DataMiningGrid system provides a flexible user interface based on Triana Workflow Editor and Manager (see Fig. 2) [6]. The workflow client is used to manipulate data resources, select applications and orchestrate all the grid services needed to execute these applications. To use the DataMiningGrid system, an end-user must have the Triana workflow editor installed and a valid DataMiningGrid certificate to establishing secure communication. The implemented data grid services facilitate integration of distributed databases, ‘on-the-fly’ data formatting to support data mining operations, performing data assays or summaries of accessed data sets, and the extension of Data Services to provide additional data preparation and transformation operations. In addition, remote file browsing and transfer utilities provide easy user access to file-based applications and are implemented using the Java CoG Kit. An important feature of the system is that it allows users to analyze their data without the need to install grid middleware or any software at the location where their data resides. Many users will have data sets that they wish to analyze, which are not already exposed as data resources on the DataMiningGrid. The DataMiningGrid is based on ex-

isting open technology such as Globus Toolkit 4 [1,2], Triana [6], OGSA-DAI [7, 8], and GridBus [8]. IV. DATA SIDE CLIENT – JOD-INTEGRATION CLIENT A special JodIntegration client was constructed using the OGSADAI-WSRF-2.2 Application Programming Interface [7,8]. It accesses data from multiple (two) Jod medical databases, prepares that data for cross-validation and converts it into the Weka Arff format. OGSA-DAI clients such as this one have been described in more detail elsewhere [7,8]. Here we give a brief overview of the developed client: 1. The user defines an SQL query to be executed against multiple Jod medical databases that reside in Slovenia. 2. This query is executed against one Jod database in order to retrieve the JDBC metadata describing the data returned by the query. 3. A temporary database table is created in a MySQL database residing at the University of Ulster. The columns of this table correspond to the columns defined by the metadata returned in the previous step. 4. The Jod medical databases in Slovenia are queried in turn. The results of these queries are each delivered to a

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separate data stream, which will be received by the data resource in Ulster. 5. The data resource in Ulster receives the data streams from Slovenia, and loads this data into the temporary database table that was previously created for this purpose. 6. The data in the temporary table is divided into test and training data sets for cross validation and converted to Weka Arff format. 7. The temporary table is dropped from the database in Ulster.

has been highlighted in a recent white paper by the European Commission entitled “Towards the Synergy between Research in Medical Informatics and Bioinformatics”. The DataMiningGrid software is now freely available under the Apache License 2.0 at SourceForge.net. It is proposed that grid technology is used as basis for the development of Medical Data Mining Grids (MEDIGRIDs).

As a result of executing this client, a set of training and test data sets are written on the user’s machine (for data mining purposes!), i.e. the machine where the OGSA-DAI client, or the corresponding Triana unit was executed. The files need to be transferred to a grid node with a functioning GridFTP [2] server before additional DataMiningGrid resources can process them. Following this part, the end-users continue executing data mining applications on the generated data and performing analysis.

We acknowledge the cooperation of all DataMiningGrid partners and collaborators in the DataMiningGrid project. This work was supported by the European Commission FP6 grant DataMiningGrid, Contract No. 004475.

ACKNOWLEDGMENT

REFERENCES 1. 2.

V. CONCLUSIONS The DataMiningGrid© system was presented and its use in the medical domain. Key features of DataMiningGrid© include flexibility, extensibility, scalability, efficiency, conceptual simplicity and ease of use. The system has been developed and evaluated on the basis of a diverse set of use cases from different sectors in science and technology. It may be expected that the presented grid-based approach will open new ways for performing on-the-fly analysis of medical data from medical hospitals throughout Europe. In this study, the DataMiningGrid was used integrate data from geographically distributed relational databases and then to perform analysis of the collected data by using powerful Grid computing facilities. Some other scientific challenges, which are also tackled by the DataMiningGrid project are research in fast distributed text classification, distributed content retrieval based on similarity analysis, automatic construction of ontologies and intelligent datamining-based Grid monitoring tasks. The undergoing research is also addressing the synergy between bioinformatics and medical informatics. This critical new development

3.

4. 5. 6. 7.

8. 9.

Open Grid Services Architecture (OGSA), http://www.globus.org/ogsa/ Foster I, Globus Toolkit Version 4 (2005) Software for ServiceOriented Systems, in Jin H, Reed D, and Jiang W, (editors): NPC 2005, LNCS 3779, pp 2-13 Stankovski V, May M, Franke J, Schuster A, McCourt D, Dubitzky W (2004) A Service-Centric Perspective for Data Mining in Complex Problem Solving Environments, HR Arabnia and J Ni (editors). Proc of Int’l Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'04), II, pp780-787 GridLab, Weka4WS at http://grid.deis.unical.it/weka4ws/ Witten IH, Frank E (2005) Data Mining: Practical machine learning tools and techniques. 2nd Edition. Morgan Kaufmann, San Francisco. Triana at http://www.trianacode.org/ Antonioletti M, Atkinson M, Baxter R, Borley A, Chue Hong NP, Collins B et al. (2005) The design and implementation of Grid database services in OGSA-DAI. Concurrency and Computation: Practice and Experience, 17(2-4), pp. 357-376 OGSA-DAI project Web site at www.ogsadai.org.uk/ under documentation GridBus Service Broker, a grid scheduler for computational and data grids, www.Gridbus.org/broker/ Author: Vlado Stankovski Institute: Street: City: Country: Email:

Univeristy of Ljubljana, FGG-KGI Jamova cesta 2 Ljubljana Slovenia [email protected]

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Availability Humanization - The Semantic Model in Occupational Health M. Molan1 and G. Molan2 1

University Clinical Centre, Institute for Occupational Health, Poljanski nasip 58, SI-1000 Ljubljana, Slovenia 2 HERMES SoftLab Research Group, Litijska 51, SI-1000 Ljubljana, Slovenia

Abstract— To connect work place with worker perception of work a special availability humanization expert model was developed (AH-model). The basics for the model development were the expert knowledge from the area of the occupational health work and the organizational psychology, human factors and ergonomics. The AH-model has been developed to support estimations of workloads, overloadness, fatigue and burn out at the working places with dominant psychical loads. With implementations of the AH-model, evaluations of implemented humanization measures are possible. The heart of the model is selfperception of actual availability. For this purpose the tool for self-estimation of actual availability has been developed. The tool and its application in the model has been validated in different working environments in Slovenia on the numerous of 3000 workers. The model development has started 14 years ago. It has been improved with the new knowledge and experiences from the implementation. The model is formalized with the graph theory and the database is constructed with implementation of ORM technology. The AH-model, together with the QAA tool for actual availability estimation, is the complex method for use in estimations of humanization measures implementation.

For this purpose particular expert models describing relation between work and worker were defined to determine the effects of work on worker and to support the evaluation of humanization interventions at the work place. Elements of the relations "work" to "worker" are various, spread and there is a huge mass of unorganised data. The process in the relation "work" to "worker" is very complex one. To describe this process the expert model reflecting activities in the relation should be a useful tool to reduce the perceived stress of human factors specialist, ergonomist and occupational health personal in the evaluation of their interventions on the relation "work" to "worker". A. The research goal The main research goal of our work was development of expert model as a tool to support the evaluation process in the relation "work" to "worker". Expert model should be a tool to describe the process, to facilitate the evaluation of humanization measures implementation and to support the decision making process in evaluation of humanization interventions.

Keywords— human factors, AH-graph, model semantic, AH-semantic, AH-model

I. INTRODUCTION In the occupational health due to the mass of unorganised, spread non-homogenous data it is difficult to estimate results of interventions in the relation "work" to "worker". The relation "work" to "worker" is the focus of interest in the occupational health. The most important activity of occupational health experts is reduction of negative impacts of working environment on worker. The process of reduction of negative impacts of work on worker is called humanization. At the working environment with objectively measurable work loads and negative impacts of working environment on worker measurement of humanization intervention results is trivial. In the modern working environments with dominantly psychical work loads and service, work measurements of humanization interventions demand the alternative approach.

II. METHOD To achieve the goal - development the expert model for application in the occupational health, to describe relation "work" to "worker" the development process has passed the following steps. A. 1st step: the description of the relation "work" to "worker" According to the expert knowledge in the area of occupational health, ergonomics, work psychology and human factors the relation "work" to "worker" should be described with the following axioms B. AH-model axioms Axiom 1: Work as the activity performed by worker is a composite of working environment influences on worker, influence of technology on worker, influence of organisa-

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Availability Humanization - The Semantic Model in Occupational Health

tion relations at the work place and influence of available human recourses (workers abilities). Axiom 2: All those four group of impact factors determine the composite work load. The composite work load describes influences of impact factors on worker manifested with workers perception of them. Axiom 3: Work load influence in the relation "work" to "worker" on worker. That relation is described with workers perception of work in the form of work load. Work load is according to this axiom workers perception of impact factors influence Axiom 4: Work load determines workers availability. With increasing of work load the availability of worker decreases. Higher level of availability reflects lower level of work load. Worker perception of his/her availability is his/her self estimation of availability to deal with work load and perform the task. Axiom 5: Worker perception of his/her availability, determine his/her performance. According to the axiom worker performance is directly determined with worker availability. Axiom 6: Worker perception of his/her availability determines his/her perception of health. Health, defined according to WHO definition, is according to the axiom directly determined with worker availability. Axiom 7: Worker performance is manifested in results of work, which have economic value. According to the axiom it is possible to economical evaluate worker performance. Decrease of performance means lower economical results and means costs. Axiom 8: Decrease of worker health should be economical validated. According to axiom decrease of worker health means costs. Axiom 9: The main focus of all activities is to shape the work load in the relation "work" to "worker" within limits which determine adequate level of worker availability. Axiom 10: Adequate level of worker availability should be according to the axiom kept with shaping of workload with intervention in working environment, in technology, in organization and in human recourse in the form of humanization interventions. Axiom 12: Humanization interventions should be economical validated. Humanization interventions cause costs.

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D. 2nd step: formalisation of the relation "work" to "worker" Elements of the relation work to worker should be presented with the graph. Elements from the space "work to worker" are presented with vertices. There are the following vertices in our 4partite graph. E. Description of graph partitions Graph partitions are: 1st partition: work (E, T, O, HR) 2nd partition: worker (PS, PF, GF, MO, VI, MD, ST) 3rd partition: output (P, H) 4th partition: humanization (HM) W - work vertices: • • • •

E - ecology T - technology O - organization HR - human recourse

AA - worker vertices: Actual availability - self perception of worker - worker vertices: • • • • • • •

PS - psychical fatigue PF - physical fatigue GF - general fatigue MO - motivations VI - vigilance MD - mood ST - perceived stress W - output vertices:

• •

P - performance H - health W - humanization vertex:

•

HM - humanization mesures

C. Description of axioms According to the proposition all costs should be compared. Costs of performance decrease and costs of health decrease are compared with costs of humanization interventions. Balance of compared costs is the ground of all activities in the relation "work" to "worker". Fig. 1 AH-Graph, 4-partite graph

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F. 3rd step determination of the syntax

I. AH-model interventions

All elements of the space "work" to "worker" are integrated on the basis of graph in the model. According to the key elements we called the model AH - availability humanization model. On the basis of AH graph conceptual data model with implementation of ORM technology was developed. Concept of the model is realised in the relational database. For research purposes prototype of the database for SQL was developed. Functioning of the developed database was verified on the real data. For AA data collection a special tool QAA (Questionnaire for actual availability) has been developed. The questionnaire has been validated on 3000 Slovenian workers in different working environments. On the collected data the database function was validated.

AH-semantics offers possibilities to describe interventions at the work place. Intervention 1: Investments in human resource in the form of individual therapeutic support. Intervention 2: Organization of work with precise definition of organizational structure Intervention 3: Individualized education and training for particular job. Intervention 4: Adaptation of working environment with introduction of good ergonomic practise. Intervention 5: Stimulation of team work and social support at the working groups.

G. 4th step introduction of models semantic All elements and connections of the AH model have interpretations which determine semantics. Special interpretation is focussed to the semantic of the humanization interventions. According to the expert knowledge of human factors experts 4 groups of elements integrated in the AH-model (presented with 4 partitions of the AH-graph) are related. H. AH-model rules All those relations and impacts are interpreted in the AHsemantics. On the basis of functioning database for AHmodel analyses of real working environment is possible. Rule 1: Work components (E, T, O, HR) influence on worker and his/her level of actual availability (PS, PF, GF, MO, VI, MD, ST). Rule 2: Performance of worker and worker health depends on worker actual availability. Decrease of worker performance and decrease of worker health increase costs of the system. Rule 3: The goal of the management of each system is reduction of costs and increase of the profit. Rule 4: Four groups of elements integrated in the AHmodel, presented with the 4th partition, are humanization measures. Rule 5: Humanization measures are interventions at the work. Humanizations measures are investments and they are costs for the system. Rule 6: Cost of humanization interventions are compared with costs of worker performance decrease and costs of worker health decrease. Rule 7: The goal of all activities is humanization of work to reduce work load and to increase perceived level of actual availability.

III. CONCLUSSION To estimate interventions at the work, at the human recourse activities and in the working environment a model describing relation "work" to "worker" was determined. The AH model development process passed a few development steps. The key elements on which development process was grounded have been: • • • •

human factors expert knowledge introduction of graph theory formal determination of AH model syntax interpretation of the relations "work" to "worker" with semantics

The output is the expert AH model, usable in human recourse management activities and in occupational health, to support evaluation of humanization interventions at the work place. Developed AH-model is the expert model for implementation in occupational health and for implementation in human factor activities. Together with the tool Questionnaire of Actual Availability (QAA), which has been developed as a part of the AH-model, it is the expert model of application in the area of occupational health. In preventive medicine, where occupational health belongs, there are huge mass of non-organized spread data with great variability. Worker should be, according to his/her role in the occupational health activities, healthy person. He/she has not as much interests like an ill person to participate in data collection. Due to these facts, development of expert models to support evaluation process and decision making in the area of human factors and in occupational health, is of great interest.

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4.

MOLAN, Marija, MOLAN, Gregor. Availability assessment for performance prediction in the real working situation. V: Proceedings of the IASTED International Coference on Applied simulation and modelling: June 28-30, 2004, Rhodes, Greece. Anaheim; Calgary; Zürich: International Association of Science and Technology for Development Press, 2004, pp. 560-565 ORM, Object role modeling, The official site for conceptual data modeling.http://www.orm.net/ Halpin, T.A. 2001a, Information Modeling and Relational Databases, Morgan Kaufmann Publishers, San Francisco (www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-672-6). Skof M. and Molan G., 1992, Human availability and system's performance in systems of high technologies. In Organization and information systems 2 by Z. Kaltneker (eds.) (Moderna organizacija, Kranj), 237–248 MOLAN, Marija, MOLAN, Gregor. Model povezave razpolozljivosti in delovnega okolja = Model for connection of actual availability and workload. Sanitas et labor, 2001, year 2, no. 1, pp. 27-41.

AH-model is focused to the key elements of the relation "work" to "worker". It is focused to worker self-perception of his/her actual availability on which depend the whole relation "work" to "worker" and to humanization. The most important mission of humanization interventions is the increasing of human actual availability at the work. Availability and humanization have to be tightly integrated and connected. The main interests of all intervention at the work are described with relation availability and humanization. The output is humanized worker friendly work. Developed AH-model presents integration of human factors expert knowledge and knowledge of information experts.

5. 6. 7.

8.

REFERENCES 1.

2. 3.

Molan M. Molan G., Expert model for prediction of human performance. In: Axelsson J, Bergman B, Eklund J, Editors. Proceedinsg of the international conference on TQM and human factors – towards successful integration. Vol 2; 1999 Jun 15-17 Linkoeping. Microsoft SQL Server 2005 Express Edition, http://www.microsoft.com/sql/editions/express/default.mspx Skof M. and Molan G., 1991, Human potential capacity, real capacity, and performance : models and connections. In Use of probabilistic safety assessment for operational safety by IAEA (IAEA, Vienna), 766–770.

Author: Dr. Marija Molan Institute: Street: City: Country: Email:

University Clinical Centre, Institute for Occupational Health Poljanski nasip 58 Ljubljana Slovenia [email protected]

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GIFT: a tool for generating free text reports from encoded data Silvia Panzarasa1, Silvana Quaglini2, Mauro Pessina3, Anna Cavallini4, Giuseppe Micieli5 1 3

Consorzio di Bioingegneria e Informatica Medica, Pavia, 2 Dept. of Computer Science and System, University of Pavia, TSD Project, Milan, 4 IRCCS Foundation “C. Mondino”, Stroke Unit, Pavia, 5 IRCCS “Humanitas”, Rozzano (MI), Italy

Abstract— The benefits of the electronic patient record versus the traditional paper-based clinical chart have been widely illustrated in the medical informatics literature. However, hospital information systems are often used only to administrative purposes (admission/discharge) and most of the clinical information is still paper-based. There is a resistance of healthcare operators to shift from paper to computer, and there are many reasons for this behaviour, that will be discussed in the paper. One of these reasons, that is also the focus of our work, is that while formulating the data model, physicians are often fostered (by computer scientists) to reduce free text data. In other words, computerisation of the medical record is often offered as an opportunity to “encoding” information as much as possible, with the promise that this will facilitate further statistics and data sharing. However, in some cases physicians perceive this encoding as a constraint and a limitation. In this paper we discuss this issue and we illustrate a solution devised for a Stroke Unit. Keywords— Electronic Patient Record, Clinical Practice Guideline, Encoding, Natural Language Generation.

I. INTRODUCTION Electronic Patient Record (EPR) is expected not only to store patients’ care history, but also to support clinical and administrative work and communication with multiple sources of medical information and knowledge. Examples of these sources are laboratory and radiology information systems (inside the same hospital), other EPRs (from different hospitals), and knowledge-based systems, i.e. software tools supporting physicians during the daily management of the patients’ careflow. Moreover, EPR is the basis for exchanging opinions with colleagues and for data transferring to general practitioners (GPs), once the patient is discharged. Need of data sharing with so heterogeneous frameworks rises several challenges to the design of the EPR data model and user's interface. For clinical routine and communication with “human agents”, of course users prefer systems based on free text data entry, being the solution closest to the usual paperbased documents approach and face to face communication. Such systems give the user flexibility and freedom to represent patient’s condition in the desired order and granularity.

Conversely, their narrative structure is hardly accessible for “software agents”, e.g. computer systems for decision making and statistical analysis, for which structured information is preferred. A great opportunity to bridge this gap is offered by emerging techniques of natural language processing [1]. Moreover, combining progress in speech recognition with availability of portable, mobile, and wireless devices, will offer the opportunity of entering information everywhere and in every moment [2,3,4]. However, according to Baud et al. [5] "automatic encoding of procedures and diagnoses by computer directly from free text is not yet a solved problem, without human intervention or validation". In particular, these techniques are not powerful enough to extract standardized and structured data from free-text in situations in which knowledge about presence or absence of a certain finding or disease is life-threatening. Opposite to the natural language processing approach, there is the possibility of encoding all the information entered in the EPR. Proposed standards are SNOMED (www.snomed.org) and LOINC (www.regenstrief.org/ medinformatics/ loinc) for terminologies, and HL7 (www.hl7.org) and OpenEHR (www.openehr.org) for data exchange; in last years there is an intention emerging to harmonise the proposed new standards. Despite clear benefits to data sharing with software agents, the “encoding” approach is still viewed as time consuming and restrictive in the daily clinical activity. There are three main problems. First, the richness and variety of medical concepts are currently a major barrier to formulating a widely accepted and standardized clinical vocabulary suitable for encoding patient specific information. Second, even where standards exist, it is not easy to find the correct term within them: for example, encoding pathologies through the International Classification of Diseases (ICD) requires navigating a complex hierarchy. Third, physicians need to produce textual information, mainly to be printed for communication purposes, for example the discharge letter. Thus, if encoding is adopted, also Natural Language Generation (NLG) techniques will be probably necessary [6,7,8,9]. The goal of our work is to reach a compromise between needs for encoding and needs for expressiveness, i.e. to obtain an EPR system which is able both to dialog with software agents, such as decision support systems, and to produce free text reports for communication, administrative

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and legal purposes. We illustrate our solution for a Stroke Unit, where a computerised version of the SPREAD clinical practice guideline (CPG) for stroke management [10] was implemented. Of course there is a need for integration with the EPR, in order to both generate suggestions according to the available patient data and verify the physicians' adherence to them. II. THE CLICNICAL APPLICATION: STAGE STAGE (Stroke Active Guideline Evaluation) is an Italian project dealing with the evaluation of the impact, in terms of physicians' compliance and patient outcomes, of the application of the clinical guideline edited on March 2005 for stroke prevention and management [10]. The project involves twenty Neurological Units in Italy which are using the same EPR, for six months without- and for the further six months with a decision-support integrated module. The idea is to evaluate physicians' compliance to the guideline, before and after providing the EPR with functionalities such as automatic generation of guideline suggestions, reminders and patients' status alerts. To obtain this goal the clinical chart has been integrated with a Careflow Management System (CfMS) developed on the basis of the SPREAD guideline [11,12]. The aim of data-entry consequently is twofold: to store patient data and to provide enough information to evaluate the adherence to the guideline (during the whole project period) and to generate decision support (during the second project phase). Before starting the project we analysed the existing EPR and its user interface: the aim was to verify the presence of the data necessary to generate guideline suggestions. Thus, the data model has been updated both by increasing information and by changing the nature of the existing information, mainly shifting from free text to encoding. The next paragraphs focus on this update, and on efforts done to meet the users’ requirements. The stroke unit EPR was WINCARE® implemented by TSD-Projects. III. WORKING ON THE EXISTING EPR In the previously implemented EPR, data-entry was mainly limited to free-text forms, as shown in the upper part of Figure 1, and encoding only concerned few specific information. Therefore EPR purpose was just to limit paperbased communication of data and improving retrieval of clinical charts. The advantage was that information was ready to be printed as-it-was for summaries, discharge letters, etc.

Fig. 1 - Shifting from free-text to encoded data entry: the picture refers to past and recent clinical histories

It is clear that no integration with the system for the guideline management was possible because of almost complete lack of structured data. In fact information stored by this EPR did not let a proper interpretation of the “rules” embedded in the SPREAD guideline: for example, still referring to the upper part of Figure 1, automatic evaluation of the eligibility of a patient to thrombolytic treatment was impossible, because it requires very clear information about clinical history. A. Encoding information To add the decision-support functionalities, reengineering of both structure and interface of the EPR was performed. A deep examination of the guideline allowed to determine the minimum data set required for implementing all the recommendations. Some of them are very crucial, for example the administration of r-tPA, the above mentioned thrombolytic drug. This treatment can save the patient's life if it is administered within few hours from the ischemic stroke symptoms onset, but its management requires both neurologists' skill and very accurate analysis and interpretation of the patient's history, because it has several contraindications. The major risk is fatal bleeding. Consequently, we must be able to capture or to exclude by sure these contra-indications from EPR. For this reason (there are many other similar situations in the guideline), to avoid misinterpretation, most of data concerning patient's history have been encoded. When possible, existing standard classifications, such as ICD9-CM for the pathologies, have been used. As shown in lower part of Figure 1, the result is a strongly structured EPR. Associated to each encoded item, the user interface allows entering free text notes, in order to

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input details that could hardly be encoded, but that are not important to decision support purposes. With the new EPR version, the interpretation of clinical guideline rules is easier and safe: for example, contraindications to thrombolysis are retrieved using specific ICD9 codes and automatic calculation can be done to know if the therapeutic time-window is still open. On the other hand, some problems have been highlighted during the pilot utilisation phase: 1) it was difficult to navigate ICD9 efficiently, despite usual facilitation for keyword- and codebased search and 2) information was no more immediately available for producing nice printed reports. Concerning the first problem, about 50 past clinical charts have been analysed in order to find the most common pathologies reported in the history of patients and their relatives. In such a way an initial list of "frequent items" has been created. Then, as long as new data are entered, this list is continuously updated by an automatic algorithm, and diversified according to sex and age. The list is shown as the first choice, and ICD9 will be accessed only if the required pathology is not in the list. Next paragraph illustrates the solution to the second problem. IV. AUTOMATIC GENERATION OF REPORTS To meet physicians' needs to quickly produce printed reports in natural language we developed a module for Generation of Inferable Free Text (GIFT). This module has been integrated within Wincare® and activated whenever the user completes a form and click on the button “Generate Report”. The idea was to create a generic module able to produce textual reports starting from a set of encoded data in whichever Wincare® form (but it should be easy to integrate GIFT into another EPR software). Code descriptions are combined with associated notes (if any), according to the underlying Italian grammatical rules, taking into account patient's and relative's gender, to generate the correct words, and suitable introduction and conjunction sentences. Once generated, the report may be edited for adjustments. Instead of creating ad-hoc report for every form, we classified Wincare forms into two groups: linear and grid forms, because of their different structure requiring different management of report creation. An example of a linear form is the Recent History in which there is a fixed-length sequence of encoded data items. An example of grid form is the Past History, in which the number of encoded items is different from patient to patient. We modeled all the information needed for the text generation within a set of relational tables (GIFT DB). The most important one is Coded-Fields, containing:

• ID: an unique id for the record; • FORM-NAME: the name of the Wincare form (for example RECENT_HISTORY); • TAG: the name of the tag associated to the Wincare field, needed for retrieving the field value; • LABEL: the label of the field; • TEXT: field-related text, which will be part of the generated report; • ORDER: concatenation order of the field in the report • TYPE: pointer to the table TIPOLOGY in order to manage punctuation marks and/or unit of measure (ANTE and POST); • INPUT: refers to the table TEXT_M_F which manages word generation according to the patient's gender (remember Italian language is very sensitive to gender, and gender-related endings are many and various); • SENTENCE: refers to the table SENTENCES. The presence of the character * at the beginning of the sentence in TEXT indicates that the sentence must be itemized in the report. According to data shown in Figure 2 and taking into account data inserted by the user in the recent history form, the report generated by GIFT will be “…The systolic blood pressure measured is: 140 mmHg. Patient’s medical history revealed motor deficit and language disturbances. She has pace maker....”. For the tag SYS_PRESSURE, using information stored in TYPE, GIFT simply concatenates the TEXT with the value inserted by the user (e.g. 140) taking into consideration punctuation mark (ANTE field “:”) and unit of measure (POST field “mmHg”). As said before, for tags MOTOR and LANGUAGE, GIFT replaces the initial character * with “Patient’s medical history revealed” taken from Sentences and then attaches the TEXT. Eventually, for PACE_MAKER, GIFT uses information stored in Text_M_F in order to put the correct subject for the sentence according to the patient's gender and puts only the text “has pace maker” because the value selected by the user was YES. In this case the table Sensible_Values has been

Fig. 2 – Attribute values in some rows of the GIFT DB.

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used: it has been created to meet physicians' need of a simple and smooth report. For each TAG in Coded_Fields, GIFT checks whether the value selected by the user belongs to the VALUE column, in this case: a) if the value is ABSENT or UNKNOWN the corresponding TEXT will not be inserted into the final report (ANTE and POST have null value); b) if the values is YES or PRESENT, the character “+” in the ANTE fields means that GIFT will only insert the TEXT, without adding the "yes" value, since the text itself is in affirmative form. GIFT has been integrated in Wincare and interaction is as follows: a) Wincare calls GIFT passing six parameters (the name of the form; the connection string to GIFT DB; the patient’s gender “M” or “F”; a reference to the form in order to retrieve the value inserted by the users; actions to be done - e.g. “ORDER BY” and/or “GROUP BY”; fields on which the actions must be executed; b) GIFT's output parameter is a string containing the produced report, which can be visualized by Wincare through its interface. The behaviour of the software module is the same in the case of linear and grid form, but for the latter GIFT has to make additional operations stated by the parameters actions and fields. Table 1 shows the performance of the system for a particular patient's history. Grey font in the "recent patient history" indicates the portion of text that has not been reported in the computer-generated report. Post-hoc analysis showed that in most cases these portions are simply redundancies, or unnecessary details. On the contrary, the "past patient history" generated by the computer is in general more extensive, mainly because it reports the full ICD9 definitions of pathologies, and not only abbreviations or suggestive symptoms, as physicians often do. This has been considered an improvement, because abbreviations are not standard and are often misunderstood. A preliminary evaluation on 10 patient’s reports has been done in a similar way and used as a feedback for further refinement. The most appreciated features have been lack of ambiguity and chronological order of the patient’s related events. Moreover, GIFT DB ensures great flexibility in report production: in principle, every user could decide his own report content and format. V. CONCLUSIONS This work shows that it is possible to mediate between needs for encoding and needs for textual reporting from EPR. We are aware that NLP is an alternative to our approach, but we want to stress the absolute need, in our application, of retrieving the correct and complete information about the patient history and objective examination. In fact, this information is used by critical suggestions generated by

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the decision support system. The project is in its first phase: we plan to collect information about the proportion of "missing" text that need to be added by hand-editing the automatic report and, in general, about users' satisfaction. Table 1 Comparison between hand-written and computer-generated reports Hand-written by physician RECENT PATIENT HISTORY This morning, at about 8:00 the patient manifested mouth deviation at left and dysartry. GP has been called that found deficit of the VII inferior right nerve and sent the patient to the emergency room of the OSM. Then she has been transferred to the stroke unit with the suspect of cerebral ischemia. Computer-generated RECENT PATIENT HISTORY On 28/1/2005, at 8:00 am the patient manifested mouth deviation at the left side and dysartry. She called her GP and then she was transferred to the stroke unit. Hand-written by physician PAST CLINICAL HISTORY In 1978 TVP, in 1989 inferior AMI. Antiplatelet and antihypertensive therapy. Hypertension detected in 1988, therapy unknown. In the same year, appearance of angina and sensation of missing heart beat. In 2002, she had an event of “mental confusion” associated to dizziness. She underwent cerebral TC which revealed left cerebellum ischemia Computer-generated PAST CLINICAL HISTORY Patient was affected: In 1978 by venous thrombosis and embolism; in 1988 by essential hypertension, therapy unknown, and by Angina Pectoris, with sensation of missing heart beat; in 1989 by acute myocardial infarction. Antiplatelet and antihypertensive therapy. In 2002 by cerebral artery occlusion, with an event of “mental confusion” associated to dizziness. Cerebral TC: left cerebellum ischemia

REFERENCES 1.

Baud R, Ruch P. The future of natural language processing for biomedical applications. Int J Med Inform. 2002 Dec 4;67(1-3): 2. Rodriguez MD, Favela J, Martinez EA, Munoz MA. Location-aware access to hospital information and services. IEEE Trans Inf Technol Biomed. 2004 Dec;8(4):448-55 3. Bergesron B. Pervasive clinical computing: data acquisition. MedGenMed. 2003 Dec 09;5(4):27. 4. Giorgino T, Azzini I, Rognoni C, Quaglini S, Stefanelli M, Gretter R, Falavigna D. Automated spoken dialog system for hypertensive patient home management; International Journal of Medical Informatics 2004 Apr 5. Baud RH, Weber P, Lovis Ch, Coding in context. PCSE 2001 Brugge 6. Huske-Kraus D. Text generation in clinical medicine--a review. Methods Inf Med. 2003;42(1):51-60. 7. Apkon M, Singhaviranon P. Impact of an electronic information system on physician workflow and data collection in the intensive care unit. Intensive Care Med. 2001 Jan;27(1):122-30. 8. Lovis CL, Lamb A, Baud R, Rassinoux AM, Fabry P, Geissbuhler A. Clinical documents: attribute-values entity representation,context, page layout and communication. AMIA Annu Symp Proc. 2003;:396-400 9. Johnson KB, Cowan J. Clictate: a computer-based documentation tool for guideline-based care. J Med Syst. 2002 Feb;26(1):47-60. 10. SPREAD (Stroke Prevention and Educational Awareness Diffusion) "Ictus cerebrale: linee guida italiane di prevenzione e trattamento", marzo 2003, www.spread.it

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Panzarasa S, Maddè S, Quaglini S, Pistarini C and Stefanelli M. Evidence-based careflow management systems. Journal of Biomedical Informatics, 35, 123 - 139, (2002) 12. Micieli G, Cavallini A, Quaglini S. Guideline Compliance Improves Stroke Outcome- A Preliminary Study in 4 Districts in the Italian Region of Lombardia. Stroke, 33, 1341 - 1347,(2002)

Author: Silvia Panzarasa Institute: Street: City: Country: Email:

Consorzio di Bioingegneria ed Informatica Medica (UPIT) via Ferrata 1 Pavia ITALY [email protected]

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Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data N. Lavrac1,2, P. Kralj1, D. Gamberger3 and A. Krstacic4 1

Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia 3 Rudjer Boskovic Institute, Bijenicka 54, 10000 Zagreb, Croatia 4 University Hospital of Traumatology, Draskoviceva 19, 10000 Zagreb, Croatia 2

Abstract— The goal of exploratory pattern mining is to find patterns that exhibit yet unknown relationships in data and to provide insightful representations of detected relationships. This paper explores contrast set mining and an approach to improving its explanatory potential by using the so called supporting factors that provide additional descriptions of the detected patterns. The proposed methodology is described in a medical data analysis problem of distinguishing between similar diseases in the analysis of patients suffering from brain ischaemia. Keywords— Exploratory data analysis, contrast set mining, subgroup discovery, supporting factors, brain ischaemia

I. INTRODUCTION Data analysis in medical applications is characterized by the ambitious goal of extracting potentially new relationships from data, and providing insightful representations of detected relationships. Methods for symbolic data analysis are preferred since highly accurate but non-interpretable classifiers are frequently considered useless for medical practice. The task of descriptive induction is to construct patterns or models describing data properties in a symbolic, human understandable form. Descriptive induction methods subgroup discovery [1], contrast set mining [2] and emerging patterns [3] are specifically designed to extract patterns (in the form of rules) from class labeled data. Unlike methods for inducing classification models (such as decision tree induction [4] and classification rule learning [5]), the patterns discovered by descriptive induction methods represent individual chunks of knowledge and are appropriate for being interpreted one-by-one. The descriptive induction task is not concluded when individual rules are discovered. A property of the discovered rules is that they contain only the minimal set of principal characteristics of the target class that distinguish the target class examples (positive examples) from the control set (negative examples). For interpretation and understanding purposes other properties that support the detected rules are

also relevant. In subgroup discovery these properties are called supporting factors. They are used for better human understanding of the principal factors and for the support in the decision making process [6]. A special data mining task dedicated to finding differences between contrasting groups is contrast set mining [2]. In our recent work [7] we have shown the similarity of contrast set mining and subgroup discovery and proposed a method for contrast set mining through subgroup discovery. The focus of this paper is to extend the concept of supporting factors from subgroup discovery to contrast set mining. We present our approach on the problem of discriminating between two groups of ischaematic brain stroke patients: patients with thrombolic stroke and those with embolic stroke. This paper is organized as follows: Section II introduces the brain ischaemia data analysis problem. Section III presents the subgroup discovery approach to contrast set mining, including the results on the brain ischemia data. Section IV presents the statistical approach to discovering supporting factors in subgroup discovery and its adaptations to contrast set mining, as well as the results and the medical interpretation of the discovered contrast sets from brain ischaemia data. II. THE BRAIN ISCHAEMIA DATA ANALYSIS PROBLEM A stroke occurs when blood supply to a part of the brain is interrupted, resulting in tissue death and loss of brain function. Thrombi or emboli due to atherosclerosis commonly cause ischemic arterial obstruction. Atheromas, which underlie most thrombi, may affect any major cerebral artery. Atherothrombotic infarction occurs with atherosclerotic involving selected sites in the extracranial and major intracranial arteries. Cerebral emboly may lodge temporarily or permanently any where in the cerebral arterial tree. They usually come from atheromas (ulcerated atheroscleritic plaques) in extracranial vessels or from thrombi in a damaged heart (from mural thrombi in atrial Fibrillation). Atherosclerotic or hypertensive stenosis can also cause a

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stroke. Embolic strokes, thrombolic strokes and stokes caused by stenosis of blood vessels are categorized as ischaemic strokes. 80% of all strokes are ischaemic while the remaining 20% are caused by bleeding [8]. The brain ischaemia database, that is the focus of our analysis, consists of records of patients who were treated at the Intensive Care Unit of the Department of Neurology, University Hospital Center "Zagreb", Zagreb, Croatia, in year 2003. In total, 300 patients are included in the database: • •

209 patients with the computed tomography (CT) confirmed diagnosis of brain stroke: 125 with embolic stroke, 80 with thrombolic stroke, and 4 undefined. 91 patients who entered the same hospital department with adequate neurological symptoms and disorders, but were diagnosed (based on the outcomes of neurological tests and CT) as patients with transition ischaemic brain attack (TIA, 33 patients), reversible ischaemic neurological deficit (RIND, 12 patients), and severe headache or cervical spine syndrome (46 patients).

Patients are described with 26 descriptors representing anamnestic, physical examination, laboratory test and ECG data, and their diagnosis. III. CONTRAST SET MINING THROUGH SUBGROUP DISCOVERY

A data mining task devoted to finding differences between groups is contrast set mining (CSM). It was defined by Bay and Pazzani [2] as "finding conjunctions of attributes and values that differ meaningfully across groups". If was later shown that contrast set mining is a special case of a more general rule discovery task [5]. Finding all the patterns that discriminate one group of individuals from all other contrasting groups is not appropriate for human interpretation. Therefore, as is the case in other descriptive induction tasks, the goal of contrast set mining is to find only the descriptions that are "unexpected" and "most interesting" to the end-user [2]. On the other hand, a subgroup discovery (SD) task is defined as follows: Given a population of individuals and a property of those individuals that we are interested in, find population subgroups that are statistically "most interesting", i.e., are as large as possible and and have the most unusual statistical (distributional) characteristics with respect to the property of interest [1]. Putting these two tasks in a broader rule learning context, note that there are two main ways of inducing rules in multi-class learning problems: learners either induce the rules that characterize one class compared to the rest of the data

(the standard one-versus-all setting, used in most clasification rule learners), or alternatively, they search for rules that discriminate between all pairs of classes (known as the round robin approach used in classification rule learning, proposed by [9]). Subgroup discovery is typically performed in a one-versus-all rule induction setting, while contrast set mining implements a round robin approach (of course, with different heuristics and goals compared to classification rule learning). Even though the definitions of subgroup discovery and contrast set mining seem different, the tasks are compatible [7]. From a dataset of class labeled instances (the class label being the property of interest) by means of subgroup discovery [1] we can find contrast sets in a form of short interpretable rules. Note, however, that in subgroup discovery we have only one property of interest (class) for which we are building subgroup descriptions, while in contrast set mining each contrasting group can be seen as a property of interest. It is easy to show that a two-group contrast set mining task CSM(G1;G2) can be directly translated into the following two subgroup discovery tasks: SD(Class = G1 vs. Class = G2) and SD(Class = G2 vs. Class = G1). And since this translation is possible for a two-group contrast set mining task, it is - by induction - also possible for a general contrast set mining task. Our experiments show that the round robin approach is not appropriate when looking for characteristic differences between two similar diseases if data about normal (healthy) people is also available. The reason is that the algorithm could – by coincidence – find features that distinguish between two diseases but are at the same time characteristic for normal people. Therefore we use a one-versus-all approach which is standard in subgroup discovery. To find characteristics of the embolic patients we perform subgroup discovery on the embolic group compared to the rest of the patients (thrombolic and those with a normal CT). Similarly, when searching for characteristics of thrombolic patients, we compare them to the rest of the patients (embolic and those with a normal CT). In this setting, we ran the contrast set mining experiment with the Orange [10] implementation of the Apriori-SD subgroup discovery algorithm [11] with the following parameters: minimal support = 15%, minimal confidence = 30%, k = 5. The results are displayed in Figures 1 and 2. Strokes caused by embolism are most commonly caused by heart disorders. The first rule displayed in Figure 1 has only one condition confirming this medical knowledge as atrial fibrillation (af = yes) as an indicator for brain stroke. The combination of features from the second rule also shows that patients with antihypertensive therapy (ahyp = yes) and antiarrhytmic therapy (aarrh = yes), therefore patients with heart disorders are prone to embolic stroke.

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Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data

Fig. 1 Characteristic descriptions of embolic patients displayed in the bar chart subgroup visualization: on the right side the positive cases, in our case embolic patients, and on the left hand side the others - thombolic and normal CT.

Fig. 2 Characteristic descriptions of thrombolic patients displayed in the bar chat subgroup visualization

Thrombolic stroke is most common with older people, and often there is underlying atherosclerosis or diabetes. In the rules displayed in Figure 2 the features presenting diabetes do not appear. The rules rather describe patients without hart (or other) disorders but with elevated dyastolic blood pressure and fibrinogen. High cholesterol, age and fibrinogen values appear characteristic for all ischeamatic strokes. IV. SUPPORTING FACTORS Exploratory pattern discovery is not concluded when individual rules are discovered. The interpretation and insightful knowledge discovery is the goal that needs to be further perused. As shown in the previous section, some rules can be interpreted directly. But the discovered rules contain only a minimal set of principal differences between the detected subset of target (positive) and the control (negative) class examples – in our case up to four features per rule. For a domain expert, in our case a medical doctor, the information about other characteristics that support and enforce the discovered patterns is very relevant. A. Supporting factors in subgroup discovery In subgroup discovery the factors that appear in subgroup descriptions are called the principal factors, while the additional properties that are also characteristic for the detected subgroup are called supporting factors. They are used for

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better human understanding of the principal factors and for the support in the decision making process [12]. The supporting factors detection process is for every detected subgroup repeated for every attribute separately. For numerical attributes their mean values are computed while for categorical attributes the relative frequency of the most frequent or medically most relevant category is computed. The mean and relative frequency values are computed for three example sets: for the subset of positive examples that are included into the pattern, the set of all positive examples, and finally for the set of all negative examples (the control set). The necessary condition for an attribute to be potentially used to form a supporting factor is that its mean value or the relative frequency of the given attribute value must be significantly different between the target pattern and the control example set. Additionally, the values for the pattern must be significantly different from those in the complete positive population. The reason is that if there is no such difference then such a factor is supporting for the whole positive class and not specific for the pattern. The statistical significance between example sets can be determined using the Mann-Whitney test for numerical attributes and using the chi-square test of association for categorical attributes. A practical tutorial on using these tests can be found in [13] (Ch. 11a and 8, respectively). The decision which statistical significance is sufficiently large can depend on the medical context. We set the cut-off values at P < .01 for the significance of the difference with respect to the control set and P < .05 for the significance with respect to the positive set. B. Supporting factors for contrast sets Even though contrast set mining and subgroup discovery are very similar, there is a crucial difference between these two data mining techniques: in subgroup discovery there is only one property of interest and the goal is to find characteristics of the individuals that have this property of interest. In contrast set mining there are several groups of individuals and the goal is to find differences between the individuals belonging to these groups. Therefore the notion of supporting factor from subgroup discovery can not be directly adopted in the contrast set mining situation. We propose and show in our experiments a way of extending the supporting factors from subgroup discovery to contrast set mining. Instead of presenting to the domain expert only the supporting factors for the positive class, we also show the distribution (for discrete) or the average (for numeric) attributes appearing in the supporting factor for the negative set and for the entire positive set. This is similar to the work presented in [14], but the methodology pro-

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N. Lavrac, P. Kralj, D. Gamberger and A. Krstacic

posed here is tailored for helping explaining contrast sets. Since the interpretation of all the patterns discovered and presented in Section III is out of the scope of this paper, we focus only on two contrast sets: Contrast set 1: (TPr=0.4, FPr=0.14) ahyp=yes & aarrh=yes → class=emb Contrast set 2: (TPr=0.56, FPr=0.2) age>66 & trig>1 & af=no & acoag=no → class=thr The first of the selected contrast sets is intuitive to interpret since both primary factors are treatments for cardiovascular disorders. The supporting factors for this set are shown in Table 1. We can see that the supporting factors (including two primary factors) for this contrast set are all about cardiovascular disorders and therefore they substantiate the original interpretation. It is therefore legitimate to say that embolic stroke patients are patients with cardiovascular disorders while cardiovascular disorders are not characteristic for thrombolic stroke patients. Table 1 Supporting factors for contrast set 1

V. CONCLUSIONS We have generalized the notion of supporting factor form subgroup discovery to contrast set mining. We have applied the proposed methodology of supporting factors for contrast set mining in the analysis of the brain ischemia domain and have achieved interpretable and useful contrast set. The experiments show how much benefit can be gained from such in depth analysis. The presented approach to the detection of supporting factors enables in depth analysis. This approach nicely supplements contrast set mining and can be also easily implemented in domains with a very large number of attributes (e.g. gene expression domains).

REFERENCES 1.

CS1

thrombolic

embolic

fo high

0.82

0.73

0.76

af = yes

80%

13%

53%

ahyp = yes

100%

81%

70%

aarrh = yes

100%

19%

45%

Table 2

factors and help the interpretation to move from speculation toward legitimate conclusions.

Supporting factors for contrast set 2 CS2

embolic

thrombolic

age high

74.2

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69.29

chol high

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5.69

6.59

fibr high

5.25

4.51

4.85

fo low

0.64

0.76

0.73

af = no

100%

47%

88%

smoke = no

73%

46%

55%

The second selected contrast set is vague and is not directly connected with medical knowledge. High age and triglyceride values are characteristic for thrombolic stroke, but the boundary values in the contrast set are not high. The rest of the features in this contrast set say no atrial fibrillation and no anticoagulant therapy: again nothing specific. The supporting factors for this set are shown in Table 2. The supporting factors include high cholesterol and fibrinogen, low fundus ocular and non smoker. These patients are old and they do not have cardiovascular disorders. These examples indicate how supporting factors enforce the primary

S. Wrobel (1997) An algorithm for multi-relational discovery of subgroups. In Proc. of the First European Conference on Principles of Data Mining and Knowledge Discovery, 1997, pp. 78–87, Springer 2. Bay S D, Pazzani M J (2001) Detecting group differences: Mining contrast sets. Data Min. Knowl. Discov., 5(3):213– 246, 2001. 3. Dong G, Li J (1999) Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In Proc. of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999, pp 43-52 4. Quinlan J R (1993) C4.5: Programs for Machine Learning, Morgan Kaufman Publishers Inc 5. Clark P, Niblett T (1989) The CN2 induction algorithm. Machine Learning, 3(4):261–283, 1989. 6. Gamberger D, Lavrac Nada, Krstacic G (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artif. intell. med.. [Print ed.], 2003, vol. 28, pp. 27-57. 7. Kralj P, Lavrac N, Gramberger D, Krstacic A (2007) Contrast Set Mining through Subgroup Discovery: Applied to Brain Ischaemina Data. In proc. of the11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2007, in press. 8. Victor M, Ropper A H (2001) Cerebrovascular disease. In Adams and Victor's Principles of Neurology, 2001, pp. 821924 9. Fürnkranz J (2001) Round robin rule learning. In Proc. of the 18th International Conference on Machine Learning, 2001, pp 146-153 10. Demsar J, Zupan B, Leban G (2004) Orange: From Experimental Machine Learning to Interactive Data Mining, White Paper (www.ailab.si/orange), Faculty of Computer and Information Science, University of Ljubljana.

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Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data 11. Kavsek B, Lavrac N (2006) APRIORI-SD: Adapting association rule learning to subgroup discovery. Appl. artif. intell., 2006, pp.543-583 12. Gamberger D, Lavrac N, Krstacic G (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artif. intell. med., 28:27-57 13. Lowry R (2007) Concepts and applications of inferential statistics. http://faculty.vassar.edu/lowry/webtext.html

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14. Lavrac N, Cestnik B, Gamberger D, Flach P (2004) Decision support through subgroup discovery: three case studies and the lessons learned. Mach. learn. [Print ed.], 2004, vol. 57, pp. 115-143. Author: Institute: Street: City: Country: Email:

Petra Kralj Jozef Stefan Institute Jamova 39 1000 Ljubljana Slovenia [email protected]

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A simple DAQ-card based bioimpedance measurement system T. Zagar1 and D. Krizaj1 1

Faculty of electrical engineering, Laboratory for bioelectromagnetics, University of Ljubljana, Slovenia

Abstract— A custom made DAQ-card based bioimpedance measurement system is presented. The signals are processed by the digital lock-in technique. The system was tested on an electrical model of skin with underlying tissues over a frequency range of 20 Hz to 1 MHz. The measurements performed directly with the DAQ-card are compared to the measurements with the instrumentation amplifier interface. The highest achieved accuracy without special calibration and compensation is about 0.5 % for the impedance magnitude and 0.02° for the impedance phase angle in the low frequency region, whereas in the high frequency region the respective values are approximately 1 % and 1°.

In this paper a design of a single channel multi frequency impedance measuring device based on the digital lock-in and the undersampling is described. The presented design is conceptually nothing new, however, the aim of the study is to present a custom made bioimpedance measuring system utilizing the devices that can be found more often than not in every laboratory and to evaluate its accuracy on a simple electrical model of skin.

Keywords— bioimpedance, electrical model, lock-in technique, measurement system

A. Employed hardware

I. INTRODUCTION There are various impedance measurement devices to choose from when measuring bioimpedance, however, some of the commercially available equipment is not suitable for direct measurement on biological samples [1]. Additionally, most of the bioimpedance measuring devices in the market are designed for specific purpose and can hardly be used for some experiments, therefore a need to design a custom device arises. Moreover, for many basic laboratory experiments there is no need to use a high-tech maximal accuracy bioimpedance measuring device – often a custom made solutions are even better suited in terms of their flexibility and adaptability to specific requirements of the problem. Electrical impedance is by definition a ratio of alternating voltage and current expressed mathematically in the complex notation, however, despite the apparent simplicity in definition a variety of methods and approaches exist to measure it. An often used method of bioimpedance measurement is a four-electrode method [2]. The current is injected into the sample through one pair of electrodes and the other pair of electrodes is used to measure the resulting voltage drop. If no current flows through the voltage measurement electrodes there is also no voltage drop across these electrodes and the measured voltage is the same as the voltage under the electrodes. Further, the devices usually employ some sort of demodulation method to measure the amplitude and the phase of a signal, which is nowadays mostly done by digital signal processing.

II. MEASUREMENT SYSTEM OVERVIEW

No application specific hardware was designed for this study. The voltage was measured directly by the USB DAQ card (NI-USB6211) and the current was also measured by the DAQ card as a voltage drop on a resistor R of a nominal value 47 Ω (Fig. 1). This two voltage measuring channels were configured to operate in a differential mode. The excitation voltage was set to a fixed value of 1.25 V and generated by an Agilent 33220A signal generator. The frequency of the generator was controlled trough the USB link with a personal computer. B. Digital lock-in technique Lock-in amplifies are commonly used to detect minute signals buried in noise [3]. The principle of the lock-in technique is quite straightforward: if the input signal vi is given by (1):

vi = A sin(ωt + ϕ ) + n(t ) ,

(1)

where n(t) is a random (white) noise, the amplitude (A) and the phase angle can be obtained by multiplication with a reference sine and cosine function of the same frequency ( ωr) as the input signal. The output of such multiplication is (2):

A [cos(ϕ ) − cos(2ωr t + ϕ )] + n(t ) sin(ωr t ) 2 (2) A = [sin(ϕ ) + sin(2ωr t + ϕ )] + n(t ) cos(ωr t ) 2

vo sin = vo cos

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A simple DAQ-card based bioimpedance measurement system

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If the output is averaged over an integer number of periods the result equals (3):

A A cos(ϕ ) + n 2 2

ωr

A A = sin(ϕ ) + n 2 2

ωr

vo sin = vo cos

sin(ϕ n ) ,

(3)

cos(ϕ n )

where An and ϕn are the amplitude and the phase angle of the spectral component of noise n(t) at frequency ωr. A signal has to be sampled over at least one complete period to get the accurate results when averaging, which means that the number of samples (N) satisfies condition (4), where k is an integer (denoting the number of sampled periods), fs is the sampling frequency and f is the frequency of the sampled signal:

N=

kf s . f

(4)

In this case either fs, f, or both of the frequencies, respectively, have to be adjusted to fulfill the condition (4). However, if the number of samples is high enough condition (4) can be violated and the average value of the signal still gets close to its true value. This is the case with presented design where the number of samples was set to 50,000. C. Sampling frequency In the presented system a fixed sampling frequency of 50 kHz was used for the frequencies above 2.5 kHz. A sampling frequency of 1 kHz was used for the frequencies below 2.5 kHz due to an inadequate settling time in the low frequency region when testing the system on the model. The number of samples in this case was set to 2000. D. Electrical model of skin and underlying tissues The system was tested on a simple electrical model of skin with underlying tissues as shown in Fig. 1. The values for the elements were chosen on the basis of previous experience in a way that the impedance of a model when measured at two terminals approximately suits the impedance of the living human skin measured at two terminals. The impedance Ze represents joined

Fig. 1 Measurement system with a simple model of skin and deeper viable tissues

electrode impedance and impedance of stratum corneum. The value for Re2 was set to 10 Ω and suits the absolute value of the impedance obtained with a precision LCR-meter when measuring two Ag/AgCl cup electrodes (cup 8mm in diameter, filled with an electroconductive gel) placed in a direct contact with each other. The value for Re1 was set to 10 kΩ and suits the value [4] obtained after several strippings of stratum corneum. This is also in accordance with [5], where the low-frequency impedance varied from 10 kΩ to 1 MΩ. A value for Ce was chosen to be 10 nF. The impedance Z represents deep viable tissues. The chosen values agree with a fact that the main electrical impedance resides in the stratum corneum while the impedance of the other layers is several orders of magnitude lower [6]. The values were: R1 = R2 = 33 Ω and Cz = 100 nF. III. RESULTS First the impedance Z (marked in Fig. 1) was measured without the Ze impedances and compared to the value obtained with an LCR-meter (Fig. 2).

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T. Zagar and D. Krizaj 0.4

70

0.2

60 70

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50 40

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amplifier ref. value without amp. 2

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Fig. 3 Impedance Z

Fig. 2 Comparison of the impedance Z measured with the DAQ-card to the

5

10

6

10

measured as shown in Fig. 1

measurement with the LCR-meter

To get an accurate value for the impedance phase angle the sampling time between the channels (conversion time) has to be accounted for (Fig. 5). There is another possible source of error in the presented model: from Fig. 1 it can be concluded that there are relatively high common mode voltages present in the system. The voltage difference to be measured is approximately 3 mV (when a voltage of 1 V is applied to the system) and is floating on the voltage of almost 0.5 V, which is about 170times higher. An amplifier with a high common mode rejection is required to amplify this signal successfully. At least in the low-frequency region the CMRR of the used DAQcard is high enough (100 dB from DC to 60 Hz) and should reject the common voltage.

magnitude error, %

3 without amp. amplifier

2 1 0 -1 -2 1 10

2

10

3

4

10 10 frequency, Hz

5

10

6

10

6 phase error, °

There is an offset in the low-frequency region of impedance magnitude, which can be compensated. The phase error from about 100 Hz to 400 Hz is the error of the LCRmeter. In the medium frequency region the error gets higher, however, this is due to the voltage dependant capacitance of the capacitor Cz used in the electrical model: the absolute value of the impedance Z measured with the LCR-meter at 1.0 V and at 0.1 V (rms values) was different by approximately 10 % and the voltage when measuring with the DAQ-card was about 0.08 V (rms) and was not controlled to be set to the same value as with the LCRmeter measurement. Further, the behavior of the designed system when the impedance of the electrodes and the skin is present was tested. The results are shown in Fig. 3. The sampling frequency was set to 50 kHz for the entire frequency span. Quite large discrepancies can be noticed in the low frequency region when measuring directly with a DAQ-card. This is due to an inadequate settling time. When measuring two channels the device has to switch a multiplexer, usually made of switched capacitors. The settling time of the channel increases if the source impedance is high. The medium frequency region follows the reference value rather well (the reference value was the value of the impedance Z measured without the Ze impedances directly with a DAQ-card, c.f. Fig. 2). In addition, Fig. 4 shows the resulting error when the sampling frequency was set as described in the sampling frequency section (II C). In this case the low-frequency error when measuring directly with a DAQ-card is almost completely vanished.

without amp. amplifier

4 2 0 -2 1 10

2

10

3

4

10 10 frequency, Hz

5

10

6

10

Fig. 4 Comparison of the reference value for the impedance Z to the measurement with an instrumentation amplifier interface and to the measurement performed directly with a DAQ-card

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A simple DAQ-card based bioimpedance measurement system

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IV. CONCLUSIONS

50

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This paper shows that it is possible to achieve a reasonable accuracy in bioimpedance measurement with a very simple setup despite the severe measurement conditions demonstrated in the electrical model. It has to be notified that absolutely no calibration procedure was performed and still the results were satisfactory, however, a calibration should be performed for the reliable measurements. Moreover, to increase the accuracy especially in the high frequency region some sort of compensation is necessary. When measuring with a specific type of DAQ-card a special attention should be paid to assure adequate settling time for the measured signals.

Fig. 5 Phase angle of the impedance Z if a delay between the channels is

REFERENCES

not taken into account

To lower the source impedance of the voltage measured by channel 1 (c.f. Fig. 1) and improve the performance of the system an instrumentation amplifier interface between the model and the DAQ-card was used (Fig. 3, solid line). A similar solution to improve the measurement accuracy was proposed by [7]. A high speed FET-input instrumentation amplifier (INA111) was employed with a gain set to about 10. The amplifier gain/phase characteristics were recorded for the treated frequency span and taken into account when calculating the impedance. The results in the low frequency region were significantly improved (the achieved accuracy is about 0.5 % and 0.1° for the impedance magnitude and the phase angle, respectively), however, the error in the high frequency region gets larger (about 2.5 % for the impedance magnitude and 4° for the impedance phase angle). In comparison to the measurement performed directly with the DAQ-card the results obtained with an amplifier interface were slightly better in the low-frequency region and worse in the high frequency region, where the accuracy of a direct measurement was about 1 % and 1° for the impedance magnitude and phase angle, respectively.

1. 2. 3. 4. 5. 6. 7.

Dudykevych T, Gersing E, Thiel F et al (2001) Impedance analyser module for EIT and spectroscopy using undersampling. Physiol Meas 22 (1):19-24 Geddes LA (1996) Who introduced the tetrapolar method for measuring resistance and impedance? IEEE Eng Med Biol 15 (5):133-134 Grimnes S and Martinsen O G (2000) Bioimpedance and Bioelectricity Basics Academic Press. pp. 188 ISBN 0-12-303260-1 Yamamoto T, Yamamoto Y (1976) Electrical-properties of epidermal stratum-corneum. Med Biol Eng 14 (2):151-158 Rosell J, Colominas J, Riu P et al (1988) Skin impedance from 1 Hz to 1 MHz. IEEE T Bio-Med Eng 35 (8):649-651 Pliquett F, Pliquett U (1996) Passive electrical properties of human stratum corneum in vitro depending on time after separation. Biophys Chem 58 (1-2):205-210 Gersing E (1991) Measurement of electrical-impedance in organs measuring equipment for research and clinical-applications. Biomed Tech 36 (1-2):6-11 Author: Tomaz Zagar Institute: Street: City: Country: Email:

University of Ljubljana, Faculty of electrical engineering Trzaska cesta 25 Ljubljana Slovenia [email protected]

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Benefits and disadvantages of impedance-ratio measuring method in new generation of apex-locators T. Marjanovic1, Z. Stare1 1

Faculty of Electrical Engineering and Computing, Department of Electronic Systems and Information Processing, Zagreb, Croatia

Abstract— One of the most critical procedures for a successful endodontic treatment is determination of root canal length. Electronic devices called apex-locators have been used for working length measurement for more than forty years. First apex-locators used direct current, but had many drawbacks. In order to improve the measurement procedure, direct current was replaced by alternating current and impedance was measured on single frequency in order to calculate the position of apical constriction. Problems with single frequency apex-locators occurred due to the presence of electrolytes in the canal. Thus, new generation of locators has been introduced – impedance is now measured on two frequencies and the position of apical constriction is calculated from corresponding impedance ratio. The aim of this paper is to examine properties of the impedance-ratio method and to compare it with single frequency measurements. Experiment was carried out in-vitro. Exact position of apical foramen on each tooth was measured with microscope. Then the teeth were placed in a freshly mixed alginate, commonly used in such measurements. Impedances were measured on frequencies commonly used by apex-locators, and with Kerr file positioned at apical foramen at several positions above and under it (in steps of 0.25 mm). Measurements were performed in dry canal and in canal filled with electrolytes usually used in endodontic treatment. Sensitivity on different type of electrolytes and sensitivity on electrode displacement were calculated for single frequency and frequency-ratio technique in order to investigate the benefits of each. By comparing them with calculated variation coefficient of raw measurements we concluded that the frequency-ratio method (used in new generation of apex-locators) is more robust to electrolytes, but its sensitivity decreases in normal condition of the dry canal. For achieving full credibility of imposed conclusion, in-vivo verification should also be performed.

constriction (also called minor foramen) is the narrowest part of root canal, 0.5 to 0.8 mm from the apical foramen (major foramen), depending on tooth type and age [1]. The apical constriction, also described as the cementodentinal junction, is the anatomical and histological landmark where the periodontal ligament begins and the pulp ends. It represents a potential natural barrier between the contents of the canal and the apical tissues (Schilder 1967) and it is generally accepted that the preparation and obturation of the root canal should be at, or short of, the apical constriction. The success of the whole treatment depends on the accuracy in determining the position of apical constriction. Today, the only known accurate way of determining it is to perform measurement after the tooth extraction. Therefore research and improvement of measuring methods is highly desirable. The most common way of root canal length determination is by using electronic methods. Many studies report on the accuracy achieved by the new generation of electronic apex-locators as well as their extended measurement capabilities in the presence of electrolytes (Fouad et al. 1993, Frank & Torabinejad 1993, Mayeda et al. 1993, Kobayashi 1995). Moreover, it is known that radiographic methods of apical constriction determination are less accurate than the electronic method (Stein & Corcoran 1992) while apical constriction is often short of anatomical apex of tooth (seen in radiograph) [1]. Electronic apex-locators work on the principle of bioimpedance measurement on one or more frequencies. One (neutral) electrode is placed on oral mucosa and the

Keywords— root canal length, electronic apex locator, impedance-ratio measuring method, electrolytes in endodontic, electrode displacement sensitivity

I. INTRODUCTION The success of endodontic treatment depends on the cleaning of root canal. The removal of all pulp tissue, necrotic material and microorganisms from the root canal requires minimal disturbance of the surrounding tissue. The apical foramen is not always located at the anatomical tooth apex (Fig. 1) and that distance can be up to 3 mm. Apical

Fig. 1 Apical anatomy

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 206–209, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

Benefits and disadvantages of impedance-ratio measuring method in new generation of apex-locators

other (active) electrode is connected to inter–canal instrument, like Kerr file, which is moved along the root canal. The impedance between the electrodes is measured and then the distance from the apical constriction is calculated. For better accuracy, significant change of impedance, used for calculation, is required when the position of Kerr file in root canal changes. On the other side, minimum influence on impedance with different teeth and lower dependency on electrolyte presence is needed. Measurement method that used bioimpedance on single frequency worked well with dry canal, but is less accurate) when commonly used agents in therapy, blood or saliva are present in the canal. For this reason, impedance-ratio method and new generation of apex-locators have been introduced. Impedance sensitivity and impedance-ratio values on electrode displacement near the apical foramen are elaborated in this paper, as well as the influence of commonly used agents and variation with different teeth. II. MATERIALS AND METHODS Measurement is performed in-vitro on twenty singlerooted teeth placed in a freshly-mixed alginate dental impression material, commonly used as a physical model for apex-locator evaluation [2, 3, 4, 5]. A simple mounting model with micrometer has been built for precise and consistent measurement. A large-area neutral electrode made of stainless steel is placed into the alginate and for active, measuring electrode, a Kerr file K-10 or K-15 is used, depending on which fits better into the foramen. Displacement of Kerr file tip from the apical foramen is controlled with micrometer. A Hewlett Packard HP4284A precise RLC meter is used for impedance measurement. Measurement and data storage are computer-controlled and both, real and imaginary part of impedance are logged on several frequencies in the range from 100 Hz to 1 MHz. Measurement range includes frequencies of 400 Hz and 8 kHz which are used by the majority of apex-locators (Root ZX for instance) and elaborated into detail in this paper. Teeth are kept in saline solution until the experiment. The position of apical foramen is precisely determined with microscope and then each tooth is placed in a freshly mixed alginate. Once the canal has been dried with paper points, the measurement of root canal impedance is performed in the range from 2 mm above the apical foramen to 0.5 mm under the apical foramen in steps of 0.25 mm. During the measurement a hysteresis is noticed, so the measurements are taken only when the file has scrolled down. Measurements are repeated for the canal moisten with saline solution and for the canal filled with 2.5% sodium

207

hypochlorite solution and calcinase, commonly used in endodontic treatment. For quantitative comparison of measuring methods, we have introduced the following parameters: sensitivity factors, coefficient of variation and estimated variability of position. The sensitivity factor of impedance S|Z| is defined as the relative impedance change ΔZ/Z on a single frequency with file-tip displacement:

SZ =

ΔZ Z . Δd

(1)

If we define impedance ratio rZ = Z(400 Hz) / Z(8 kHz), then the sensitivity factor of impedance-ratio Sr is defined as the relative change in impedance ratio ΔrZ/rZ with displacement Δd:

Sr =

ΔrZ r Z . Δd

(2)

The coefficient of variation cv is defined as the ratio of the standard deviation σ to the mean µ:

cv =

σ μ

.

(3)

For the purpose of rough estimation of variability in measured position, we define an estimated variability of position Δl for both, single frequency and impedance–ratio, methods as:

Δl =

cv . S

(4)

III. RESULTS AND DISCUSSION Frequency dependence of impedance magnitude differs significantly for each tooth, but they all track a similar curvature. For comparison purposes, impedances are normalized and results for clinically important file displacement of the apical foramen are plotted on Fig. 2 to 4. The majority of single frequency apex-locators work on 1 kHz or around this frequency. It is obvious that they have great sensitivity [6] in normal clinical condition of a dry canal when file approaches apical foramen (Fig. 2 to 4) – relative change in magnitude of impedance when moving file-tip is 92 %/mm. But in the situation when the canal is not sufficiently dried immediately before taking

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dried canal wet canal hypochlorite calcinase

0,75

0,50

0,25

0,00 100

1000

10000 Frequency [Hz]

100000

1000000

0,75

dried canal wet canal

0,50

hypochlorite calcinase

0,25 0,00

Normalised impedance

1,00

Fig. 2 Influence of canal filling, 1.0mm above apical foramen

100

1000

10000 Frequency [Hz]

100000

1000000

1,00

5,00

Fig. 3 Influence of canal filling; 0.5mm above apical foramen

dried canal wet canal hypochlorite calcinase

1,00

Normalised impedance 0,25 0,50 0,75 0,00

measurement, problems can occur – relative change in magnitude of impedance when moving file-tip drops to around 30 %/mm on 1 kHz, depending on type and amount of moisture; as presented later in Table 1. Two widely accepted approaches exist in case of two or more frequency apex-locators [1, 7]: voltage difference and impedance ratio method. Voltage difference method uses absolute difference of impedance on two frequencies (which incorporates the information on presence of the electrolyte). The sensitivity of impedance change with file-tip displacement remains the same as in already discussed case of single-frequency measurement. In case of ratio method apex-locators, the impedance ratio on two frequencies represents the position of the file in root canal and it is required that this ratio significantly depends on file-tip displacement, but not on the presence of electrolytes in the canal and on the type of a tooth. The majority of instruments that use this method (widespread Root ZX for example) take measurements on frequencies of 400 Hz and 8 kHz. The ratio on these frequencies is analyzed here. Impedance–ratio shows significantly less change in moistened canal (Fig. 5) than in a dry canal, but generally lower relative sensitivity to file displacement of apical foramen can also be noticed (about 60 %/mm, Table 1). Except for decreased sensitivity of impedance–ratio method, measurement on different teeth showed even higher variation coefficient of impedance–ratio than is the variation coefficient of impedance on a single frequency. With an estimated variability of position Δl defined as in (4) and according to Table 1, we can conclude that single frequency method gives best results when measurement is performed in normal conditions of dry root canal. However, ratio method is more accurate if root canal is moistened.

100

1000

10000 Frequency [Hz]

100000

1000000

Fig. 4 Influence of canal filling; file at apical foramen

dried canal wet canal hypochlorite calcinase

Impedance ratio 2,00 3,00 4,00

Normalized impedance

1,00

0,50

0,00 -0,50 -1,00 -1,50 -2,00 Displacement of file tip to apical foramen [mm]

Fig. 5 Influence of canal filling on impedance–ratio

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Benefits and disadvantages of impedance-ratio measuring method in new generation of apex-locators

Table 1

impedance ratio

single frequency

parameter

Comparison of measuring methods in presence of electrolyte unit

electrolyte in canal dry

hyp.

calc.

cv , Z

-

0,69

0,51

0,63

SZ

% / mm

92

25

18

Δl Z

mm

0,75

2,04

3,5

cv ,r

-

1,04

1,13

1,17

Sr

% / mm

54

69

74

Δl r

mm

1,92

1,64

1,58

Minimal error (in millimeters) can be expected in single frequency method in case of dry root canal. When electrolyte is present, accuracy of single frequency method drastically decreases. However, if impedance-ratio method is used, better results are achieved when conductive media is present in the canal.

Besides the sensitivity to electrode displacement and variation in measured parameter (impedance or impedanceratio) which cause dispersion in determination of apical foramen position (Δl|Z|, Δlr), a systematic error in measurements can also be noticed when electrolyte is present in the canal. Fig. 5 shows that in case of impedanceratio method, the mean reading of working length is expected to be about 0.3 mm longer than it really is when electrolyte is present. In contrast, if single frequency measurement is used, readings will be shorter with conductive media in the canal (for example –0.5 mm on 5 kHz). The differences between a dry root canal and a canal filled with electrolyte are expected to be much lower in reallife than presented in this paper. Normally, only insufficiently dried (blown) root canal can be expected, and here, for research purposes, canals were filled to the top. Conclusions are made on the basis of physical model and it is necessary to compare them with in-vivo measurements to achieve its full credibility [8].

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dry root canal. Impedance-ratio method has better tolerance to electrolytes in the canal, but it also shows lower accuracy in normal conditions. In real applications root canal is never fully-filled with agents (like in this research) and their lower influence is expected. Thus, if there is no assurance that root canal is dry (canals with perforations, bleeding teeth), impedance-ratio method with the risk of lower accuracy is advisable. Although the accuracy of contemporary apex–locators is sufficient for most cases, situation is not perfect and further elaborations of single frequency methods, as well as new measuring methods developments are encouraged. For the credibility of drawn conclusions, further research and in-vivo verification is required.

REFERENCES 1. 2.

3. 4. 5. 6.

7.

8.

Gordon MP, Chandler NP. (2004) Electronic apex locators. Int Endod J 7(7): 425-7 Jenkins JA, Walker War , Schindler WG, Flores CM. (2001) An in vitro evaluation of the accuracy of the root ZX in the presence of various irrigants. J Endod. 27:209–11 DOI 10.1097/00004770-200103000-00018 Kaufman AY, Keila S, Yoshpe M. (2002) Accuracy of a new apex locator: an in vitro study. Int Endod J. 35:186–92 DOI 10.1046/j.1365-2591.2002.00468.x A. Y. Kaufman, S. Keila & M. Yoshpe (2002) Accuracy of a new apex locator: an in vitro study. Blackwell Int End J 35:186–192 A. ElAyouti & C. Löst (2006) A simple mounting model for consistent determination of the accuracy and repeatability of apex locators. Int Endod J 39: 108–112 Z. Stare, T. Protulipac (2003) Sensitivity of the Root Canal Impedance to Electrode Displacement – in vivo and in vitro Measurement, MEASUREMENT Proc. of the 4th Int. Conf., Smolenice, Slovak Republic, 2003, pp 230–233 K. C. Nam, S. C. Kim, S. J. Lee et al. (1991) Root canal length measurement in teeth with electrolyte compensation. Med and Biol Eng and Comp, 40(2):200-204 DOI 10.1007/BF02348125 Stare Z, Lacković I, Galić N (2001) Evaluation of an in vitro model of electronic root canal measurement, Proc. of the 9th Mediterranean Conf. on Med. and Biolog. Eng. and Comput., Pula, Croatia, 2001. pp: 1047–50

IV. CONCLUSION Two measuring methods are compared in this paper: absolute impedance and impedance-ratio methods. For the purpose of quantitative comparison, quality factors were introduced and accordingly it has been concluded that single-frequency method gives best results in conditions of

Author: Institute: Street: City: Country: Email:

Tihomir Marjanovic Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia [email protected]

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Bioimpedance spectroscopy of human blood at low frequency using coplanar microelectrodes J. Prado, M. Nadi, C. Margo and A Rouane Laboratoire d’Instrumentation Electronique Nancy (LIEN), Nancy University, France. Abstract— Dielectric properties of biological substances are usually deduced ex vivo by the way of the impedance measurement of a cell loaded by the investigated medium. At low frequency it is well known that the bioimpedance depends on the polarization effects that occur at the electrodes interface. Measurements being affected for frequencies lower than 50 kHz for standard electrodes, black platinum was used to decrease polarization effects. In this paper, dielectric properties of blood measured at different temperatures are presented for frequencies varying between 100 Hz and 1 MHz. Keywords— bioimpedance spectroscopy, microelectrodes, polarisation effect, dielectric properties, blood

I. INTRODUCTION Impedance Spectroscopy of biological tissues has been previously inestigated as a technique for non invasive assessment of tissue characterisation [1]. Measurement of dielectric properties may be deduced from the bioimpedance measurement obtained by the interaction between an electromagnetic field source and a biological sample. In the frequency range up to 10 MHz, current conduction through tissue is mainly determined by the tissue structure, i.e. the extra- and intra-cellular compartments and the insulating cell membranes. Therefore, changes in the extra- and intracellular fluid volumes are reflected in the impedance spectra [2]. Different electrodes configurations are used to measure bioelectric phenomenon. One can distinguish between two basic functional types, macroscopic or microscopic measurement. Electrodes for macroscopic characterisation consist in bioimpedance measurement of a biological tissue sample or organ. Electrodes for microscopic characterisation were more recently [3] used for bioimpedance measurement of biological cell or very small cells aggregate. Microscopic electrodes may be used to characterize extracellular and intracellular fluids, or cell membrane. In this paper we are dealing with biological cell scale with the goal of optimizing the interface between electrodes and biological cells agregate at low frequency. The contact of the electrodes with the biological tissue or the electrolyte leads to electrochemical phenomena. This results in a charge distribution in the immediate vicinity of the electrodes and thus in an additional impedance called

“polarization impedance” [4]. This well known phenomena is specific for a given electrode-electrolyte interface. The potential due to the polarisation impedance is dependent upon he metal-electrolyte combination, current density, and frequency [5]. Since water is the primary constituent of both in vivo and vitro fluids, it is generally assumed that electrode interface impedance in these fluids is similar to that observed for physiological serum. Considerable data describing the impedance at the electrodes-solution interface are available [6]. In therapeutic or diagnostic applications or in studies on biological effects of the electromagnetic radiations, dosimetric evaluations are greatly affected by the precision of dielectrics parameters values of biological tissues. These parameters are sensitive to many influencing factors like temperature of the target organ. However, these effects remain misunderstood and the measured values are sparse, at various frequencies and exist only for some organs as compiled in [7]. II. MATERIAL AND METHODS Many methods and techniques for the measurement of complex impedance exist and are described in the litterature. In this work, the auto balancing bridge technique associated to a microsensor based on a multielectrodes matrix was applied to determine the frequential variation of a complex impedance. In the present study, we intented to measure the dielectric properties of blood for a frequency range between 100 Hz and 1 MHz in order to complete previous work at higher frequencies [8]. A. The microsensor The microsensor consists of an array of sixteen platinum microelectrodes for measurement with two reference microelectrodes on its surface. That kind of geometry has been employed previously for neural activity recording or stimulation [9], [10], [11]. We have adapted it (Figure 1) for impedance measurements of solutions. Thus, new design strategies had to be considered in order to optimize the probe performances for bio-impedance measurements at low frequencies [13].

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a PC (Figure 2). Data acquisition and analysis were managed using HPVEE© software. III. RESULTS C. Characterization of the sensor

Figure 1: view of the sensor with its tank The microelectrodes matrix consists of an eighteen microelectrodes (2-ground reference, 2 sets of 8 active electrodes) placed on a plate of substrate glass (1 x 1.2cm). This configuration provides the advantage of an easy fabrication by using standard microelectronic technologies which implies high reliability, low costs and the possibility to integrate other sensors and electronics on the same probe. The active microelectrodes are squares with dimensions of 100x100µm and 180 nm thickness. Microelectrodes reference are rectangular with 1x2.5mm of surface and 180nm thickness. This cell measurement was produced at the Centro Nacional de Microelectronica (CNM-University of Barcelona). The technological process consists in two photolithographic steps starting from a thermal oxydation to grow a thick field layer (800 nm) on 4-in. (~10 cm) P-type ‹100› Si wafers with a nominal thickness of 525 µm and is presented in [8]. B. Electronic Instrumentation The basic measurement system, based on an impedancemeter to which a microsensor is connected, is not described here in details. This sensor is a measurement cell including a Plexiglas tank for the solution under test, the electronic card for data acquisition, the impedancemeter and

S en so r

D a t a a cq u i si tio n electro n ic bo a rd

The bioimpedance depends on the frequency and the polarization effects that occur at low frequency. In this paper, dielectric properties of physiological serum are determined by a monopolar system. The polarization resistance and capacitance of electrodes in contact with standard solutions (Potassium chloride) has been investigated. A set of measurements were made at 37°±0.5°C from 100 Hz to 1 MHz range using a commercial impedancemeter. The measurement microsensor has coplanar platinum electrodes square of 100µm x 180 nm in height, the applied voltage level being 25mV. Electrolyte solution of known conductivity was used to characterize the measurement cell. A theoretical model of the impedance has been calculated using finite elements method (FEM) for each electrode. The numerical simulation was used to determine the cell factor of the microelectrodes. Effects of the polarization on the measured impedance and the dielectric characteristics were also investigated. Measurements are affected for frequencies lower than 50 kHz for standard electrodes in Platinum. This limit due to the polarisation impedance decreases to 10 kHz when, as it is well known, black platinum is used [14]. D. Measurement of blood electrical properties Measurements were done using standard Platinum electrodes and electrodes covered by black Platinum for both satndard solutions, animal and human bloods. We present here, as an example, only the relative permittivity and the electric conductivity obtained for human blood at different temperatures between 100 Hz and 1 MHz using microelectrodes covered by black platinum (Figure 3).

I m p éd a n cem eter

PC

Figure 2: View of the sensor and its electronic conditionning

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1,2

100000000 10000000

1

εr

1000000

εr

0,6

10000 1000

0,4

σ

100

σ (S/m)

0,8

100000

0,2

10 1

0 0,1

1

10

100

1000

Fréquence (kHz) εr 25°C

εr 37°C

εr 42°C

σ 25°C

Figure 3: Electric properties of human blood with platinum IV. DISCUSSION AND CONCLUSION For the impedance obtained with the microelectrodes covered with a black platinum layer, approximately 50% of the polarization effects was eliminated for frequencies greater than 10 kHz. But for frequencies lower than 10 kHz the electrode electrolyte interface influenced clearly the measurement of the bioimpedance. These results have shown that the measurement cell may be characterized by determining the conductivity of a dielectric properties with less than 20% error for a frequency over 10kHz when the microelectrodes are covered by black platinum. The conductivity has an error mainly due to the temperature effects, the electrode/electrolyte impedance, electrodes geometry of the measurement cell. The imaginary part of the complex impedance is strongly influenced by the parasitic capacitances at the microelectrodes interface and thus the permittivity at these frequencies remains to high and difficult to measure. The measurement cell was used for a small sample of blood in this first step with only one active electrode and a reference electrode. The next step will be to develop comparative measurements between single cells using a multielectrodes configuration since there is no interaction or cross-talk between the micro-electrodes. The use of microelectrodes opens up new areas in biomedical applications. Using microelectrodes arrays, it

σ 37°C

σ 42°C

covered by Pt-noir

should be possible to monitor cell movement or characterise dielectric properties of biological cell. Understanding their electromagnetic behaviour, the nonlinear phenomena or analyzing electromagnetic properties of an isolated cell versus an aggregate of cells [15], are a few examples of benefits that bioimpedance spectroscopy applications will offer from miniaturization in the near future.

ACKNOWLEDGMENT We thank the Centro Nacional de Microelectronica (CNM) for the fabrication of the measurement cell and PhD. Antoni Ivorra and M.S. Rodrigo Gomez from Barcelona University for their help and advices.

REFERENCES 1. 2. 3.

4.

Schwan H P 1963 Determination of biological impedances - Chapter 6 Physical techniques in biological research 6 Academic press Foster K R and Schwan H P 1996 Dielectric properties of tissues Chapter 1 Handbook of Biological Effects of Electromagnetic Fields 2ème edition Ed :Polk C. et Postow E CRC Press 27-102 Gomez R, Bashir R, Sarikaya A, Ladish M R, Sturgis J, Robinson JP, Geng T, Bhunia A K, Apple HL and Werely S 2001 Microfluidic Biochip for Impedance Spectroscopy of Biological Species Biomedical Microdevices 3:3 201-209 Fricke H 1932 The theory of electrolytic polarisation Phil. Mag. 14 310-318

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Bioimpedance spectroscopy of human blood at low frequency using coplanar microelectrodes 5.

Schwan H P 1966 Alternating current electrode polarisation Biophysik 3 181-201 6. Grimnes S and Martinsen O G 2000 Bioimpedance and Bioelectricity basics Academic Press. 7. Gabriel C., Gabriel S and Corhout E (1996a), “ The dielectric properties of biological tissues:I. Literature survey ”, Phys. Med. Biol., vol 41 pp 2231–49. 8. Jaspard F and Nadi M 2002 Dielectric properties of blood: an investigation of temperature dependence, Physiol. Meas. 23 547-554 9. Ivorra A, Gomez R, Noguera N, Villa R, Sola A, Palacios L, Hotter G and Aguilo J 2003 Minimally invasive silicon probe for electrical impedance measurements in small animals Biosensors Bioelectron. 19 391–9 10. Borkholder DA, Bao J, Maluf NI, Perl ER and Kovacs GT 1997 Microelectrode arrays for stimulation of neural slice preparations J Neurosci Methods 77 61-66 11. Kovacs G T A, Storment C W and Rosen J M 1992 Regeneration Microelectrode Array for Peripheral Nerve Recording and Stimulation IEEE Trans. Biomed. Eng. 39 893-902

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12. Ackmann J J and Seitz M A 1984 Methods of complex impedance measurements in biologic tissue CRC Crit. Rev. Biomed. Eng. 11 281-311. 13. Markx G H and Davey C L 1999 The dielectric properties of biological cells at radiofrequencies Applications in biotechnology Enzyme and Microbial Technology 25 161-171 14. Mcadams ET and Jossinet J 1991 Electrode-electrolyte impedance and polarisation Innov. Tech. Biol. Med. 12 11-20 15. Pavlin M, Miklavcic D. Effective conductivity of a suspension of permeabilized cells: a theoretical analysis. Biophys. J. 85: 719-729, 2003. NADI Mustapha Electronic Instrumentation Laboratory of Nancy Nancy University BP 239, Bd des Aiguillettes 54506, Vandoeuvre les Nancy France [email protected]

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Dielectric properties of water and blood samples with glucose at different concentrations A. Tura1,, S. Sbrignadello1, S. Barison2, S. Conti3, G. Pacini1 1

ISIB-CNR, Padova, Italy, 2 IENI-CNR, Padova, Italy, 3 BC Dynamics, Milan, Italy

Abstract - Impedance spectroscopy has been proposed as possible approach for non-invasive glycaemia monitoring. However, few quantitative data are reported about impedance variations related to glucose concentration variations, especially below the MHz band. Furthermore, it is not clear whether glucose directly affects the impedance parameters or only indirectly by inducing biochemical phenomena. We investigated the impedance variations in glucose-water and glucose-blood samples, for increasing glucose values (up to 300 mg/dl). In all the frequency range (0.1–107 Hz) glucose-water samples showed huge impedance modulus increases for increasing glucose values (up to 135%). In blood the impedance modulus showed only slight variations (2%), but again in a wide frequency range. Therefore: i) glucose directly affects the impedance parameters of solutions; ii) the influence on the impedance seems to decrease in high conductivity solutions, but it is still clearly present. Keywords - Impedance spectroscopy, glycaemia, diabetes, monitoring, non-invasive

I. INTRODUCTION In recent years the measurement of tissue and blood impedance through an alternating current has been suggested as a non-invasive approach to determine glycaemia [1]. In [2], it was shown that variations in blood glucose concentration determine significant changes in the impedance of a subject’s skin and underlying tissues in a range between 1 and 200 MHz. However, the authors claimed that the observed impedance changes were not due directly to glucose but to biochemical reactions triggered by variations of glucose concentration, which cause variations in the electrolyte balance across the membrane of erythrocytes. In other studies, however, impedance variations were found in glucose-water solutions with different glucose concentrations, despite no cell component was present. This was observed even at glucose concentration values that mimic glycaemic levels in human blood [3]. On the other hand, in [3] the impedance differences were observed only in a relatively narrow frequency range. These partially contradictory results show that it is not completely clear whether glucose directly affects the impedance behavior of a solution, especially when

physiological concentration levels are considered. The aim of this study was to examine possible impedance variations in solutions at different glucose concentrations within the physiological range. We studied glucose solutions both in pure water and in blood in an in vitro context. Special attention was devoted to the analysis of low frequencies, which were poorly investigated in previous studies, especially in blood. II. MATERIALS AND METHODS A. Preparation of samples A sample of deionized water (18.5 MΩ · cm resistivity, Millipore MilliQ Element system, Billerica MA, USA) was prepared. The same water was used to prepare three glucose-water samples, at glucose concentrations spanning from normal glycaemia to that observed in severe diabetes, i.e. 100, 200, 300 mg/dl. Each sample consisted of 50 ml of water. D-glucose (99.5%, Fluka) was added to the water samples to reach the indicated concentrations. For the preparation of blood samples we collected 500 ml of bovine blood immediately after the animal slaughter. In the blood container we had previously poured 1 g of potassium oxalate (99.98%, Sigma-Aldrich) and 1.25 g of sodium fluoride (99.99%, Sigma-Aldrich), acting as anticoagulant and glycolytic inhibitor, respectively [4]. We then measured the glucose concentration of the blood sample by two portable glucose meters (Freestyle, TheraSense, and Glucomen, Menarini Diagnostics). We performed two measures for each meter: the average value was 65 mg/dl. Blood was then stored into a refrigerator at 4 °C. In the following hours we checked again the glucose concentration several times: the differences compared to the first measures were always within the precision of the meters, thus confirming that glycolysis was properly inhibited. Then, we properly added D-glucose to obtain blood samples with concentrations similar to those indicated above. B. Impedance measurement Within 72 hours from sample preparation we performed the impedance measures through a Solartron 1260 impedance analyzer. For the measurement cell a probe from

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Dielectric properties of water and blood samples with glucose at different concentrations

Delta OHM was chosen (SP06T model). As shown in Fig. 1, the cell is characterized by four platinum electrodes for separation between stimulation and sensing terminals, thus allowing minimization of possible secondary effects (such as inductance of cables or parasitic capacitances) that can influence the accuracy of the impedance measurement [5]. The electrodes are then surrounded by a bell: when the cell is immersed into the sample to be studied, the measurement region is delimited and kept constant. The cell also includes a temperature sensor. The cell constant is 0.7. Through the Solartron 1260 we applied a 100 mV r.m.s. voltage to the outer couple of electrodes. The electric current was read through the inner electrodes. We analyzed the impedance of the samples in the 10-1–107 Hz range. The impedance was measured in five frequency points for each decade. For each sample studied, we performed two independent measures: after the first measure the cell was cleaned before immersing it again into the sample. The impedance values presented for each sample are the average between the two measures. All the impedance measures were performed with the samples at ambient temperature (23 °C with maximum variations of ±0.3 °C). All the measures were corrected through open-short compensation technique. III. RESULTS The impedance modulus of water, and of glucose-water mixtures, is reported in Fig. 2. The modulus increased for increasing glucose concentration values in a wide frequency range. More precisely, the frequency range where the differences were more evident (we define it as reference range) was 0.1–800 Hz. Outside this range, the differences were less clear, as the modulus curves showed relatively frequent intersections at some frequency values. As regards the phase, a decrease was observed for increasing glucose values, though variations were less marked than those observed in the modulus. The reference range for the phase

195

Fig. 2 Impedance modulus for glucose-water samples (pure water: empty circle; 100, 200, 300 mg/dl glucose: full circle, triangle, square, respectively) was 80 Hz–107 Hz, though from 105 Hz onwards the differences were small. Percentage difference values between the blank sample and that at the highest glucose concentration for both modulus and phase in their own reference ranges were 125±17% (mean±standard deviation) and 43±56%, respectively. Maximum and minimum values were: 135% and 63% for the modulus; 157% and 0.1% for the phase. As regards blood, when looking to the modulus and phase curves in the whole frequency range, almost no variation can be appreciated for the different samples. However, when the analysis was focused on a specific (though still wide) frequency range some differences emerged. In fact, in the 8– 2·106 Hz frequency range there was a slight but evident difference in the impedance modulus: similarly to glucosewater samples, the modulus increased for increasing glucose concentrations, as shown in Fig. 3. Outside the reported reference range, the modulus curves showed frequent intersections. Similar analysis for the phase showed that there was again a relatively wide frequency range, i.e. 2·105–8·106 Hz, where a slight phase decrease for increasing glucose concentration was observed in the whole range. Thus, in a frequency range which almost covers all the studied range at least one between impedance modulus and phase showed slight but clear variations for increasing glucose concentrations. Percentage difference values between the sample with endogenous glucose only and that at the highest glucose concentration for both modulus and phase in their own reference ranges were 2.00±0.09% and 1.51±0.11%, respectively. Maximum

Fig. 3 Impedance modulus for glucose-blood samples in a portion Fig. 1 Inner part of the measurement cell

of the studied frequency range (blood alone (65 mg/dl glucose): empty circle; 100, 200, 300 mg/dl glucose: full circle, triangle, square, respectively)

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A. Tura, S. Sbrignadello, S. Barison, S. Conti, G. Pacini

and minimum values were: 2.24% and 1.68% for the modulus; 1.60% and 1.27% for the phase. IV. DISCUSSION In the recent years a strong effort has been carried on aimed at the development of techniques for non-invasive glucose measurement [1]. Some of these approaches have lead to the production of non-invasive glucose meters [6], but for several reasons many of them remained at prototype level. The only one available today is the GlucoWatch [7], and it has several drawbacks [6]. A promising approach for non-invasive measurement of glycaemia is impedance spectroscopy. Some device prototypes have been developed based on this approach, and one of them also reached the market [1,8,9], but it was withdrawn and the company filed for bankruptcy [10]. A new company seems to be working on a similar device [11,12], but at the moment no device is on the market. In [2] the authors claimed that the measurement of glycaemia through impedance spectroscopy is possible as variations in blood glucose concentration induce some transportation phenomena of electrolytes through the cell membrane, and that results in variations in the dielectric properties of the medium. The most relevant phenomenon is the plasma sodium concentration lowering in the presence of hyperglycaemia [13]. In [2] it was claimed that these effects are entirely responsible for the impedance variation of blood and underlying tissues, since glucose variations do not directly affect the dielectric properties of the investigated medium in the MHz band, as also stressed in other studies from the same research group [14,15]. In fact, some references were provided to other studies where the effect of variations in glucose concentration was studied in water [16,17]. In [17] it was shown that at glucose concentrations lower than 1 g/cc the dielectric properties of the glucose-water solution are not different from those of pure water. However, a more recent study contradicts these findings. In [3] the dielectric properties of glucose-water solutions were found different for glucose concentration values varying within the physiological range. In particular, the impedance modulus increased for increasing glucose concentrations within the 1 kHz – 1 MHz band. The first aim of our study was to reproduce the reported experiments on glucose-water solutions. Our results essentially confirm those of the study [3], but in even wider frequency band: in fact, in all the investigated range, i.e. 0.1 Hz – 10 MHz, we observed a significant variation in the impedance modulus, phase, or both, though the greater differences were found for frequencies lower than 100 kHz. Thus, we can claim that variations in glucose concentration even at low values such as physiological ones directly affect

the dielectric properties of a solution, independently from other mechanisms that may be induced by glucose variations. On the other hand, it is confirmed that the impedance variations due to variations in glucose are certainly more evident at low frequencies, and this may partially explain why they were not observed in the studies [16-17] where frequencies over 1 MHz were considered. It must also be noted that the partial differences between our results and those of study [3] may be due to the use of a different measurement cell. In fact, we used a four electrodes cell instead of simple two electrodes cell, thus allowing four terminal measurements less prone to noise effects at medium-high frequencies [5]. Furthermore, we used platinum instead of stainless steel electrodes, the latter being more sensitive to the effects of possible reactions with the solution at low frequencies. Few data can be found in the literature on impedance in blood at different frequencies related to glucose concentration values. In [2] some impedance data were reported from an in vivo experiment on humans where glycaemia was 100 and 200 mg/dl, though only frequencies above 1 MHz were investigated. It was shown that both impedance modulus and phase were different between the two glucose concentration values in some frequency ranges, and similarly to our results higher values were found for the 200 mg/dl concentration. As regards the modulus, which was the impedance parameter of major interest, it was claimed that the sensitivity to glucose changes was between 20 and 60 mg/dl glucose/Ω, and this was in acceptable agreement to our results, though the sensitivity that we found was slightly lower. In fact, the best sensitivity that we observed for the modulus between the 100 and 200 mg/dl samples was about 110 mg/dl glucose/Ω (100 mg/dl: 72.2 Ω; 200 mg/dl: 73.1 Ω), at frequencies around 1 kHz. In [3] the dielectric properties of blood for different glucose concentrations were studied in vivo on hamsters, and variations in the dielectric parameters were observed for glucose concentrations varying between 150 and 300 mg/dl. However, only one frequency value was investigated (10 kHz), and only semi-quantitative results were reported. Some studies investigated the dielectric properties for different glucose concentrations of PBS buffers with suspended erythrocytes [14,15]. In [14] different glucose concentrations were considered ranging from zero to about 400 mg/dl, and the analysis was performed between 10 kHz to 100 MHz. Variations in the dielectric properties of the buffers were found for the different glucose concentrations. However, the dielectric parameters showed a nonmonotonic pattern for increasing glucose values, differently to our results. In [15] the analysis was extended to 2 GHz, with similar findings. The authors claimed that this non-

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Dielectric properties of water and blood samples with glucose at different concentrations

monotonic behavior may be due to erythrocytes activities at the membrane level, but no further details were provided. In our study huge variations in the impedance parameters for different glucose values were observed only in water. This suggests that the ability of glucose to induce variations in the dielectric properties of the solution may depend on the conductivity levels involved. In fact, in blood solutions, which have conductivity much higher than water, the impedance variations were modest compared to those in water, but still clearly present. In conclusion, this study investigated the effect of glucose concentration on the impedance of different solutions, i.e. glucose in water and in blood. Few studies showed the impedance variations of blood for different glucose concentrations within the physiological range, and to our knowledge no study examined in detail the frequency values below the MHz band: this is one of the main novelties of this study. The advantage of focusing on frequency values below the MHz for possible future clinical applications may consist in a lower sensitivity to the electromagnetic noise in the environment. The study showed that glucose is able to directly affect the impedance of the investigated samples. In blood, slight but clear impedance variations for different glucose values were observed in a wide frequency range, and especially below 1 MHz. Possible indirect mechanisms involving cells may only contribute to the observed total variations.

5.

6.

7.

8.

9. 10. 11.

12.

13.

ACKNOWLEDGEMENTS The authors thank Dr. Franceschini for supply of bovine blood, and Dr. G. Sbrignadello and Dr. M.C. Scaini for their useful comments and help. The study was partially supported by a grant from Regione Veneto (DGR 2702/1009-04) and from CNR in the framework “Ricerca Spontanea a Tema Libero” (Research number: 946).

14. 15.

16.

REFERENCES 1. 2.

3. 4.

Khalil OS (2004) Non-invasive glucose measurement technologies: an update from 1999 to the dawn of the new millennium. Diabetes Technol. Ther. 6: 660-697 Caduff A, Hirt E, Feldman Y, Ali Z, Heinemann L (2003) First human experiments with a novel non-invasive, nonoptical continuous glucose monitoring system. Biosens Bioelectron. 19:209-217 Park JH, Kim CS, Choi BC et al. (2003) The correlation of the complex dielectric constant and blood glucose at low frequency. Biosens Bioelectron 19:321-324 Chan AY, Swaminathan R and Cockram CS (1989) Effectiveness of sodium fluoride as a preservative of glucose in blood. Clin Chem 35:315-317

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Awan SA, Kibble BP (2005) Towards accurate measurement of the frequency dependence of capacitance and resistance standards up to 10 MHz. IEEE Trans Instrum Meas 54:516520 Tura A, Maran A and Pacini G (2006) Non-invasive glucose monitoring: Assessment of technologies and devices according to quantitative criteria. Diabetes Res Clin Pract. In Press (DOI: 10.1016/j.diabres.2006.10.027) Tierney MJ, Tamada JA, Potts RO et al. (2001) Clinical evaluation of the GlucoWatch (R) biographer: a continual, non-invasive glucose monitor for patients with diabetes. Biosens Bioelectron 16:621-629 Pfutzner A, Caduff A, Larbig M et al. (2004) Impact of posture and fixation technique on impedance spectroscopy used for continuous and noninvasive glucose monitoring. Diabetes Technol Ther 6:435-441 Weinzimer SA (2004) PENDRA: the once and future noninvasive continuous glucose monitoring device? Diabetes Technol Ther 6:442-444 Wentholt IM, Hoekstra JB, Zwart A et al. (2005) Pendra goes Dutch: lessons for the CE mark in Europe. Diabetologia 48:1055-1058 Forst T, Caduff A, Talary M et al. (2006) Impact of environmental temperature on skin thickness and microvascular blood flow in subjects with and without diabetes. Diabetes Technol Ther 8:94-101 Caduff A, Dewarrat F, Talary M et al. (2006) Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy, Biosens Bioelectron 22:598-604 Hillier TA, Abbott RD and Barrett EJ (1999) Hyponatremia: evaluating the correction. factor for hyperglycemia. Am J Med 106: 399-403 Hayashi Y, Livshits L, Caduff A (2003) Dielectric spectroscopy study of specific glucose influence on human erythrocyte membranes. J Phys D: Appl Phys 36:369-374 Caduff A, Livshits L, Hayashi Y (2004) Specific D-glucose Influence on Electric Properties of Cell Membrane at Human Erythrocyte Studied by Dielectric Spectroscopy. J Phys Chem B 108:13827-13830 Fuchs K, Kaatze U (2001) Molecular dynamics of carbohydrate aqueous solutions. Dielectric relaxation as a function of glucose and fructose concentration. J Phys Chem B 105:2036-2042 Mashimo S, Miura N, Umehara T (1992) The structure of water determined by microwave dielectric study on water mixtures with glucose, polysaccharides, and L-ascorbic acid. J Chem Phys 97:6759-6765 Corresponding author: Author: Institute: Street: City: Country: Email:

Andrea Tura, PhD ISIB-CNR Corso Stati Uniti, 4 Padova Italy [email protected]

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FENOTIP: Microfluidics and Nanoelectrodes for the Electromagnetic Spectroscopy of Biological Cells V. Senez1, A. Treizebré1, E. Lennon3, D. Legrand2, H. Ghandour1, B. Bocquet1, T. Fujii3 and J. Mazurier2 1

IEMN/CNRS-USTL-ISEN, University of Lille, 59652 Villeneuve d’Ascq, France 2 UGSF/CNRS-USTL, University of Lille, 59652 Villeneuve d’Ascq, France 3 CIRMM/IIS, University of Tokyo, 153-8505 Tokyo, Japan

Abstract— This work is focused in the area of ligandreceptor interaction analysis. The purpose is to be able to assign, in real time, a specific crossing pathway to a ligand/receptor pair, without the use of molecular labels. The classification is based on changes in the electrical properties of the cells. Various BioMEMS have been designed and fabricated in order to characterize the variation of the electrical properties of the biological cells. We are interested in both dielectric (i.e. polarization) and vibrationnal (i.e. absorption) spectroscopy. Several devices are currently tested for low (<10MHz) and high (>40GHz) frequency range (LFR & HFR) measurements. In the LFR, we have fabricated coplanar and 3D electrodes sensors for impedance measurements. In the HFR, we have designed and processed coplanar waveguides. In the LFR, we have performed static and dynamic measurements on small cluster of cells. In the HFR, we have shown that we can propagate microwaves along submicrometer single wires (Goubau propagation). We are going to use these HFR devices for measurement on small cluster of cells. Keywords— Living Cell, Cell Signaling, Dielectric & Vibrationnal Spectroscopy, Biomems, Nano-Electrode and wire.

I. INTRODUCTION Biological cell analysis is a very important field of research. Currently, the prevailing paradigm to analyze cellular functions is the study of biochemical interactions using fluorescence based imaging systems. However, the elimination of the labeling process is highly desirable to improve the accuracy of the analysis. Recent developments in microand nanofabrication technologies are offering great opportunities for the analysis of biological cells; the combination of micro fluidic environments, nano-electrodes/wires and ultra wide band electromagnetic engineering will soon make possible the investigation of local (submicrometer scale) dynamic processes integrating several events at different time scales. In the paper, we present our work which aims at investigating living-cells with the help of MEMS and NEMS (Micro and Nano Electro Mechanical Systems) and ultra wide band (DC-THz) electromagnetic characterization techniques. We are working with a well-characterized biological model

(ligand: lactoferrin, receptors: nucleolin and sulfated proteoglycans) [1]. We propose to follow the various phases of assembly between the ligands and receptors present or not on a set of mutant CHO cell lines and internalization of the ligands/receptors complexes into the cells (Fig. 1). II. MICRODEVICES AND TECHNOLOGIES Permittivity measurements across a range of frequencies provide information about the species and their chemical environment. Features in dielectric spectrum (the polarization relaxation) are classified as α-, β- and γ-dispersion. In the HFR range, the spectrum may exhibit rotationabsorption phenomena. For biological species, access to a broad frequency range is absolutely necessary due to their size and chemical diversity. That is why we are currently investigating different types of structures. We have tested various methods for the immobilization of the cells: i) mechanical (Fig. 2a), ii) chemical (Fig. 2b) and 3) electrical (Fig. 2c).

Fig. 1 Binding of lactoferrin on two mutants CHO cells. CHO 677 line does not allow endocytosis of lactoferrin.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 170–173, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

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Fig. 3 Impedance microsensor (4-points): a) 3D silicon and planar gold electrodes, b) 3D and planar gold electrodes. Channels are made of PDMS.

Fig. 2 a) Single cell measurement set-up using deformability of cell between 3D electrodes; b) Patterning and immobilization of CHO cells on glass substrate by surface treatment with OTS and amine; c) Microfluidic chip for dielectrophoretic immobilization of single cells. In the low frequency range (LFR) (<10MHz), we have designed and fabricated coplanar and 3D electrodes sensors for impedance measurements (Fig. 3a-b). In HFR, we are using coplanar slotline (CS) (Fig. 4a) and we are also investigating waveguide coupled to Goubau line (GL) (Fig. 4b). GL is a single wire line showing low loss transmission thanks to the excitation of a surface electric wave [2]. BioMEMS are fabricated on silicon or glass substrates with microfluidic parts in polymeric materials. Different polymer materials are available (e.g.: polycarbonate, polyethylene, polymethylmethacrylate, polystyrene, polydimethylsiloxane (PDMS)).

Fig. 4 HFR devices: a) slot lines on PPTMDSO; b) Coplanar wave guide to Goubau line on quartz.

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V. Senez, A. Treizebré, E. Lennon, D. Legrand, H. Ghandour, B. Bocquet, T. Fujii and J. Mazurier

Fig. 5 Gold planar waveguide is deposited on PPTMDSO substrate. The inset shows the texture of gold.

We are using usually PDMS which is optically transparent, amenable to micromolding, biocompatible and has excellent O2 and CO2 permeability. However, it has several disadvantages. It shows fairly high losses (εr≈2.7, tanδ≈0.04 in V and W-bands). Its Young modulus is extremely low (<5MPa). Its metallization requires surface treatment (i.e.: with teflon like material). That is why we have selected Plasma Polymerized TetraMethylDiSilOxane (PPTMDSO) [3]. A new technological process based on cold remote nitrogen plasma (Fig. 5) allows us to obtain 50-80µm thick layers with a rigid texture and a very good adhesion to silicon substrate. This technological process is now well defined and is compatible with a classical microelectronic process.

Fig. 6 Advanced model based on transport lattice method for cell-electric field interaction in microfluidic channel: a) geometry and mesh of the structure, b) Magnitude and phase of the electrical impedance calculated with SPICE.

III. MODELING AND MEASUREMENT The accuracy of the electromagnetic characterizations depends on the correct design of the sensors (i.e.: coplanar electrodes or waveguide, 3D electrodes, GL, etc…). In the LFR, one can solve Laplace equation in combination with the finite element method (FEM). However, for realistic dielectric model of the cell, geometrical details prevent the use of FEM due to memory and solver limitations and we are developing an approach based on Kirchhoff laws and transport lattice method (Fig. 6a-b) [4]. In the HFR, one can solve the Maxwell equations and obtain the spatial distribution of the electric and magnetic field along the CPW, SL and GL (Fig. 7a-b)). Simulations have been performed with the Microwave Studio software from CST. In the future we have to work on the high frequency modeling of the cell. In the LFR, we have already performed measurements on small cluster of cells and single cell using planar gold electrodes or 3D silicon electrodes (Fig. 8a-b).

Fig. 7 a) Distribution of the electric field and b) reflexion and transmission coefficients for a microwave guide obtained by solving Maxwell equations with Microwave Studio from CST.

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impedance of the cell’s membrane (i.e.: capacitive). Current experiences aims at studying the effect of receptor-ligand (i.e. lactoferrine-proteoglycans) interactions on the impedance of the membrane. Very preliminary results show a decrease of the impedance during the ten first minutes after the introduction of lactoferrine and a recovery within 30 minutes. In HFR, on-wafer measurements (Fig. 9) have been made with Vectorial Network Analysers (VNA) (see inset in Fig. 9) covering the [40MHz-110GHz] and [140GHz220GHz] bands on a back-to-back structure (Fig. 4). One can see that the propagation of the wave is experimentally demonstrated regarding the value (-5dB at 140 GHz) of transmission parameter (S21). These preliminary results show that GL are promising sensors for BioMEMS applications. The next step is to performed measurements on single cell with this GL.

ACKNOWLEDGMENT

Fig. 8 a) SEM picture of the 3D electrodes used to characterize the single cell, b) Ratio of the amplitudes of the electrical impedance (cell+medium/medium) versus frequency.

Parts of this work have been carried out within the FENOTIP project funded by the French Research National Agency (ANR) as PNANO n°05-0244-A3 and the grant of Dr. E. Lennon was supported by the Japanese Society for the Promotion of Science (JSPS).

In Fig. 8, one can observe that the sensor can detect the presence of the cell between the electrodes in the [1KHz1MHz] range. This impedance increase corresponds to the

REFERENCES 1.

2. 3.

4.

Fig. 9 a) Simulated and measured reflexion (S11)/transmission (S21) coefficients of a Goubau line (GL) measured with a vectorial network analyzer (inset), b) Schematic of the biomems under fabrication incorporating the GL line and the microfluidic channel for single cell characterization.

Legrand D, Vigié K, Said E., Elass E., Masson M., Slomianny M-C., Carpentier M., Briand J.P., Mazurier J. and Hovanessian A., (2004) Surface nucleolin participates in both the binding and endocytosis of lactoferrin in target cells, Eur. J. Biochem. 271, 303–317. Wang K. and Mittleman D., (2004) Metal wires for terahertz wave guiding, Letters to. Nature, 432, 376-379. Mille V., Bourzgui N.E., Medjdoub F., Bocquet B., (2004) Design of silicon-pTMDS bio-MEMS with millimeter and sub-millimeter waves transducers, 34th European Microwave Conference, 1, 169-172. Smith K., Gowrishankar T., Esser A., Stewart D. and Weaver J., (2006), The spatially distributed, dynamic transmembrane voltage of cells and organelles due to 10~ns pulses: meshed transport networks, IEEE Trans Plasma Sci., 34,1394-1404.

Author: Institute: Street: City: Country: Email:

Vincent Senez IEMN/CNRS Avenue Poincaré – BP 60069 59652 Villeneuve d’Ascq France [email protected]

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Impedance method for determination of the root canal length D. Krizaj1, J. Jan2 and T. Zagar1 1

University of Ljubljana, Faculty of Electrical Engineering, Lab. for bioelectromagnetics, Trzaska 25, 1000 Ljubljana, Slovenia 2 University of Ljubljana, Medical School, 1000 Ljubljana, Slovenia

Abstract— Accurate root canal length determination is crucial for successful endodontic procedure where the root canal of the tooth is opened, cleaned and sealed. Impedance method is becoming a standard method for determination of the position of apical foramen. Current devices rely on determination of apical foramen from the ratio of impedances measured at two or more frequencies. The investigation is aimed in determination of optimal parameters for determination of apical foramen with best possible accuracy. The investigation has revealed an optimal ratio of 0.79 determined at frequencies 1 kHz and 500 Hz with a standard deviation of about 1 mm. Keywords— root canal, impedance method, ratio method, impedance spectroscopy.

as a measure for determination of the root canal length. Several investigations have been published evaluating the devices for measuring the root canal length (for instance Czerw et al., 1995; Haffner et al. 2005, Venturi and Breschi, 2005, etc.) as well as the method itself (Križaj, 2004). In general the results indicate the method is exact up to a certain accuracy. The aim of former investigations was mostly focused in comparisons of different types of apparatus (devices) and not the method itself. In order to advance our understanding of the operation of these devices as well as further improve the measurement techniques we analyzed the impedance between the file and the oral mucosa by using impedance spectroscopy.

I. INTRODUCTION Root canal length determination of a human tooth using measurement of electrical impedance between the file inserted into a root canal of the tooth and an outside electrode applied to the oral mucosa is becoming a standard method in endodontics – a special branch of dentistry. Exact assessment of root canal length is a crucial factor for successful endodontic treatment. Several methods have been proposed to improve the accuracy of the measurements and several investigations have been performed to evaluate the devices on the market. On the other hand, not much research has been devoted to the investigation of the technique itself and its limitations. The impedance method is tempting due to its simplicity of use as well as low cost of the device, however, due to large variation between electrical and morphological properties of the teeth the technique can be regarded accurate only up to a certain limit. The first attempts to use the measurement of electrical resistance between the tip of the file placed inside the root canal to the electrode placed on the oral mucosa go back the to year 1962. Sunada (Sunada, 1962) found that the tip of the file reaches the apical foramen when the electrical resistance dropped to about 6.5 kΩ. It was however difficult to obtain consistent values especially due to the polarization effects at the surface of the electrodes that occurs at direct (constant) current. An improvement was achieved by an approach suggested by Kobayashi (Kobayashi et al., 1991). Two frequencies of 0.4 kHz and 8 kHz were used and the ratio instead of difference between the impedances was used

II. METHOD Figure 1 presents a measurement set-up with the file inserted in the root canal serves as one electrode, the second electrode was placed in the saline surrounding the extracted tooth. Fourteen extracted teeth (10 incisors, 2 canines, 2 premolars) from adults (mean age 57 years, range 45-75 years) were used for the study. They were further separated to two groups: 8 tooth from adults older than 50 years and 6 tooth from adults younger than 50 years (the youngest had 45 years). The data was analyzed separately for the two groups as well as altogether. The teeth had matured root apices and a single root canal configuration. Actual canal lengths were established by advancing a size 10 K-file apically until the tip of the instrument was just visible at the apical foramen.

Quadtech 1920 PRECISION LCR METER

electrode 1

-

+

fixation

electrode 2 NaCl

Fig. 1: Measurement set-up.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 174–177, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

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Fig. 3: Impedance ratio at frequencies 50 kHz and 500 Hz. Thick line represents average of all measured curves together with standard deviations.

Fig. 2: Absolute impedance measured at frequency of 50 Hz for 14 teeth. solid line:older than 50 years, broken line: younger than 50 year.

The tooth prepared for measurements was placed in a normal saline solution and fixed with light-cured composite resin. Impedance was measured between the size 10 K-file inserted in the root canal and the outer metal electrode, placed in the saline solution. A Quadtech 1920 precision impedance analyzer (LCR meter) in a frequency range from 20 Hz to 1 MHz was used for impedance measurements. The measurements were repeated at different distances of the file tip from apical foramen: 8, 6, 4, 3, 2, 1.5, 1, 0.5, 0, -0.5, -1, and -2 mm. III. RESULTS For each tooth the tip of the file was gradually set to exact canal lengths and the impedances were measured. After the impedances were measured for all canal lengths they were analyzed as a function of the canal length. Figure 2 presents measured absolute values of impedances for 14 teeth (8+6) at a selected frequency of 1 kHz and varying canal lengths. It can be seen that the impedance was for most measurements almost constant up to about 2 mm from the apical foramen and than decreased with decreasing canal length. Canal length was measured from the apical foramen towards the crown. When the tip of the file reached the apical foramen an additional decrease of the impedance occurs. However, this decrease was not so large and appeared at different values of the impedance (from 1 kΩ to 10 kΩ). This makes determination of the apical foramen directly from the measured impedance inaccurate. With dashed line are indicated the results for teeth younger that 50 years and with solid lines the teeth older than 50 years. On average the teeth older than 50 years gave larger impedances between the file and the saline.

Currently the most frequently used approach for determination of the root canal length is to measure impedance at least two different frequencies and use a ratio of impedances at different frequencies as a measure of the canal length. An example of this approach can be seen in Figure 3 for a ratio of impedances Z ( f 2 ) / Z ( f1 ) for a pair of frequencies f2 = 50 kHz and f1 = 500 Hz. In all cases a significant reduction of the ratio of impedances appeared when the tip of the file approached the apex. Typically this drop was observed at lengths smaller than 1 mm. Apical foramen (zero length) could be determined at a certain range of ratios, from about 0.6 to 0.4, indicating that for a fixed ratio, several different canal lengths could be obtained. This technique can thus not be expected to be absolutely accurate but exact up to certain accuracy. The thicker line in Figure * represents an average for all 14 samples with added standard deviations. On average a value of impedance ratio of about 0.5 seems to be the optimal choice. On the other hand, for optimal determination of apical foramen it is more important to find a ratio of impedances that would give the smallest standard deviation from on average exactly determined zero canal length (apex). Therefore, we analyzed the data in such a manner that we started from optimal average ratios at apical foramen and searched values around it to verify if a smaller standard deviation of measured distance from apical foramen can be obtained. Ten pairs of frequencies were investigated denoted with letters from A to J for 5 different frequencies (0,5 kHz, 1 KHz, 5 kHz, 10 kHz and 50 kHz). As a starting point we used the average ratios obtained at apical foramen (let us call them M) and searched for a smallest mean distance and standard deviation in its vicinity (M+0.05, M-0.05 and M0.1). In this manner we obtained for each set (from A to J) four average canal lengths and its standard deviations (not shown in Figure 4). A number from 1 to 4 is added to the letters from A to J to indicate these data. As both, minimal average canal lengths as well as minimal standard deviation

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If we choose a very simple criterion function, that a sum of the average ratio and its standard deviation should be minimal (min(SD+X)). Using this criterion for all measured teeth the set H3 has been found the optimal choice following with the set E4 and A3. The set H3 might however not turn out to be the favorable choice since only a small number of samples fall in the ± 0.5 mm range and only for 11 samples a length can be determined. On the other hand for all 14 samples of the set E4 a length could be determined and 7 of them fall in the ± 0.5 mm range. Set E4 has ratio determined from frequencies 5 kHz and 1 kHz at an impedance ratio of 0.79. Comparable result is achieved for a set A3 determined at frequencies 1 kHz and 0.5 kHz at an impedance ratio of 0.91.

Average distance from apical foramen [mm]

2 1.5 1 0.5 0

-0.5 -1

A

B

C

D

E

F

G

H

I

J

Fig. 4: Average distance from apical foramen for different sets of ratio

IV. CONCLUSION

values and frequencies.

are required for optimized canal length determination, we evaluate them in the XY plot where the X-axis represents the average canal length and the Y-axis the standard deviation shown in Figure 5. For example, A2 represents results (average distances) for impedance ratios Z (1 kHz) / Z (500 Hz) determined at the ratio 0.91 and A1 for a ratio of 0.91+0.05. A certain criterion function is required to determine the optimal set of frequencies and the optimal ratio. This criterion should be set by the clinicians. The results show that standard deviation and minimal average canal length are correlated. Smaller standard deviation can be expected for reduced average canal length. Quite a nice linear relationship can be set as SD = -0.8X+1.1.

Standard deviation = SD [mm]

3.5 3

y = 0.88*x + 1.1

J3 I3 J1

2.5 2 1.5 1 0.5 0 -1

A3 H2 A4

G2 F1 D3 D2 J4 G3 C1 G4 D4 B1 I4 F2 C2 E1 B2 F3C3 B3 A1 C4 E2 H1 E3B4 A2F4 E4

J2 I2 G1

I1

An impedance method used for evaluation of the root canal length has been investigated. Teeth of subjects older than 45 and younger than 75 years were used in the study. The impedance between the file inserted in the root canal and the electrode placed in the saline was measured at frequencies from 20 Hz to 10 MHz. Impedance ratio was investigated as a measure for evaluation of the root canal length. An attempt was made to determine the set of frequencies that would yield the best accuracy in determination of the root canal length. A trade-off between minimal average distance from apical foramen and its standard deviation is necessary. For this purpose a simple criterion function was set as a minimum of a sum of average distance from apical foramen and its standard deviation. Using this criterion function with an additional criterion that at the selected impedance ratio the distance from the apical foramen could be determined for all teeth the best results were obtained for a ratio of impedances equal to 0.79 measured at frequencies 5 kHz and 1 kHz. In this case an average distance from apical foramen is –0.07 mm with a standard deviation of 1.04 mm.

D1

REFERENCES 1. 2.

-0.5

0 0.5 1 Average distance = X [mm]

1.5

2

Fig. 5: Standard deviation and average distance for all evaluated impedance ratios.

3.

Czerw RJ, Fulkerson MS, Donnelly JC, Walmann JO (1995) In vitro evaluation of the accuracy of several electronic apex locators. J Endod 21(11):572-5. Haffner C, Folwaczny M, Galler K, Hickel R (2005) Accuracy of electronic apex locators in comparison to actual length--an in vivo study. J Dent 33(8):619-25. Križaj D, Jan J, Valenčič V (2004) Modeling AC current conduction through a human tooth. Bioelectromagnetics 25(3):185-95.

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Impedance method for determination of the root canal length 4. 5. 6.

Kobayashi C, Suda H (1994) New electronic canal measuring device based on the ratio method. J Endod 20(3):111-114. Sunada I (1962) New method for measuring the length of the root canal, J Dent Res 41:375-380. Venturi M, Breschi L (2005) A comparison between two electronic apex locators: an in vivo investigation. Int Endod J 38(1):36-45.

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Author: Institute: Street: City: Country: Email:

Dejan Krizaj Faculty of Electrical Engineering Trzaska 25 1000 Ljubljana Slovenia [email protected]

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Impedance Spectroscopy of Newt Tails F.X. Hart1, J.H. Johnson2 and N.J. Berner2 1 2

Department of Physics, The University of South, Tewanee, USA Department of Biology, The University of South, Tewanee, USA

Abstract— We use impedance spectroscopy to characterize the electrical properties of newt tail. The newt model has attracted recent interest because the regeneration of newt tail and limbs may provide insights into tissue engineering for mammals. Impedance spectroscopy could provide a convenient means to monitor the progress of regeneration while various controlling stimuli are applied. In this initial phase of our research we compare the impedance spectra of healthy and necrotic tail tissue and identify a potentially confusing artifact in the spectra due to interneedle capacitance. Keywords— impedance, newt, regeneration.

I. INTRODUCTION Impedance spectroscopy has been used to characterize a wide range of tissue properties. Detailed information regarding its use and the underlying theory may be found in a recent review article [1]. In particular, this technique can be used to monitor the progress of various physiological changes in biological systems [2 – 5]. Our long term goal is to use impedance spectroscopy to monitor the regeneration process in newt tails. Here we describe differences in the dielectric spectra of normal and necrotic tissue in the tail of a newt (Notophthalmus viridescens viridescens). The conductivity of the necrotic tail tissue is less than that of healthy tissue, in contrast to what is commonly observed during tissue degeneration. Moreover, we identify and explain an artifact related to electrode capacitance which appears at high, rather than low frequencies. Tail regeneration in the newt occurs in three phases: wound healing and differentiation; blastema accumulation and growth; and differentiation and morphogenesis [6]. In this process a crucial aspect is the formation of a blastema, which contains mesenchymal stem cells that are able to differentiate to form a regenerate. The blastema begins to grow early in the process of regeneration, around 5-11 days after amputation [7] at the site of the lost appendage. Complete tail regeneration generally takes between 6 and 8 weeks, with a greater rate of tail elongation in newts with more proximal amputations than in those with more distal amputations (see references in [7]). The rate of regeneration is also highly dependent on the temperature at which the experiment is conducted. N. viridescens is usually kept at

25 degrees Celsius which is considered the temperature most comfortable for the species (see references in [8] ). The newt model provides special insights for regenerative medicine. Brockes and Kumar [9] have compared regeneration in salamanders and mammals. Differentiated mesenchymal cells in the blastema exhibit plasticity and dedifferentiate into other cell types. These regenerating cells also display positional memory in that they regenerate tissue proper to their original site when transplanted elsewhere. The mechanisms for these processes are not well understood. Brockes and Kumar express the hope that insights obtained from the study of regeneration in the salamander, and the factors which control it, will lead to advances in mammalian tissue engineering and, perhaps someday, to the engineering of a mammalian founder blastemal cell. Determining which factors affect regeneration must involve monitoring the progress of blastema development and subsequent tissue growth while various chemical and physical stimuli are applied. Modalities such as functional MRI, PET scans, or x-ray imaging are suitable for monitoring local changes in blood flow patterns, uptake of drugs, or structure. There is a need for a modality which can examine changes in larger-scale physiology while leaving the examined tissue unaffected by their use. We are beginning a study of the potentiality of impedance spectroscopy for this purpose and report here some of our initial findings. II. MATERIALS AND METHODS Eastern red spotted newts (Notophthalmus viridescens viridescens, family Salamandridae) were collected by dip net in Franklin County, Tennessee. Newts (2 – 4 g total mass) were kept in the laboratory in aquaria with 4 – 5 cm filtered, conditioned tap water (treated for chlorine, chloramine, and excess ammonia) at about 10°C in incubators (Percival I-30BLL Biological Incubators). Newts were exposed to natural light-dark cycles and fed cut-up mealworms (Tenebrio sp.) ad libitum. For experimentation, newts were wrapped in a moistened paper towel with only their tail exposed and placed in a clamp lined with foam rubber. Thus the animals were held firmly, and the tail was readily available. Newt tails became necrotic when the spinal cord was inadvertently severed

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Impedance Spectroscopy of Newt Tails

during a bleeding protocol (not for these experiments). Necrotic tails were red posterior to the cord lesion. The most necrotic tail was missing some flesh ventrally. We performed a series of preliminary measurements to characterize and compare the dielectric spectra of healthy and necrotic newt tail tissue. A pair of nickel-plated brass needles, diameter 0.60 mm and separation 13.7 mm. served as the electrodes. We embedded the non-pointed ends of the needles in a small, 20 mm thick plexiglass block to maintain mechanical rigidity. The pointed ends of the needles extended 13.0 mm beyond the block into a piece of styrofoam to ensure complete penetration of the tail. We connected the electrodes to a Solartron 1260 Impedance Analyzer, which was controlled by a Dell computer using the program Z60 from Scribner Associates. The Solartron applied 76 frequencies between 1 Hz and 30 MHz with amplitude of 50 mV. The data were first analyzed using the program ZVIEW from Scribner Associates and then transferred to the program Microsoft Excel for more extensive modeling. III. RESULTS

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Fig. 1 Impedance Spectrum of a healthy newt tail. Squares, ReZ; Circles, ImZ; Dashed lines indicate the fits provided by the circuit shown in Figure 3.

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Figure 1 illustrates an impedance spectrum taken from the tail of a healthy newt. Similar results were obtained from tails of other healthy animals. Three dispersions are apparent. The low frequency dispersion below about 100 Hz is presumably due to the electrode-tissue interface. A weak dispersion near 100 kHz could be attributed to the bulk tissue. These two dispersions have been common features of impedance spectra measured, in-vivo, on frog gastrocnemius [10], octopus arm [11] and crayfish tail muscles [12]. However, the third dispersion centered near 10 MHz does not appear in these muscle spectra. To determine the origin of this third dispersion we submerged the electrodes in ordinary tap water and obtained the spectrum shown in Figure 2. The ReZ values are constant, as expected, between about 10 Hz and 100 kHz. The decrease in ReZ at higher frequencies and the corresponding peak in ImZ, which appears around 400 kHz, must be an artifact. We noted that in the previous muscle studies the inter-needle capacitance was lower because of the larger needle separation used and the resistance was lower because the muscle tissue was more conductive. In those cases the lower resulting RC time constant would have created an artifactual dispersion appearing at frequencies beyond the range of the Solartron. In the present study this artifactual dispersion has been pushed down into the frequency measurement range. Further measurements with different electrode separations and water conductivity confirmed this hypothesis.

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Log freq. (Hz) Fig. 2 Impedance Spectrum of tap water. Squares, ReZ; Circles, ImZ; Dashed lines indicate the fits provided by the circuit shown in Figure 3.

The equivalent circuit used to model the impedance spectra is displayed in Figure 3. A series of two parallel combinations of a resistor and a constant phase element (CPE) represents, respecively, the electrode/tissue interface and the bulk material, which is in this case the newt tail. A resistance Rhi-f is added to account for the real part of relaxations which occur above 1 MHz. The complex impedance of a CPE is given by Z* = A(iw)-n, where w = 2πf, i = √-1, A and n are parameters, and the * refers to a complex-valued quantity. A parallel CPE/R combination produces in the impedance spectrum a peak in ImZ and a knee in ReZ. This combination is the circuit realization of a Cole-Cole dispersion. The series CPE sequence shown in Figure 3 has been used to describe the electrical behavior of a wide variety of biological [12 - 14] and non-biological systems [15, 16]

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Zel

Zbulk Rhi-f

of the other parameters change, but not by a large amount. The increase in resistances is unexpected. We performed similar measurements on a second newt with a necrotic tail and obtained similar, though somewhat smaller increases in resistance. 7

Rel

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Fig. 3 Equivalent circuit used to model impedance spectra.

This model can be extended to the present situation by the addition of a parallel, interelectrode capacitance as shown in Figure 3. The capacitance per unit length of a pair of long, parallel, circular cylindrical electrodes of radius r and separation S is C/l = πKeo/ln(S/r – 1), where eo is the permittivity of free space and K is the dielectric constant of the medium. We approximate the relatively short needles as a parallel combination of a pair of long cylinders embedded in plexiglass and a pair of long cylinders embedded in either water or tissue. For the tap water results shown in Figure 2 this model predicts an interneedle capacitance of about 95 pF. The dashed lines in Figure 2 illustrate the excellent fit to the measured spectra obtained using the model shown in Figure 3. We used the following parameters to achieve this fit: Ael = 3.7x105, nel = 0.85, Rel = 10 MΩ; Rbulk = 11 kΩ; Rhi-f = 0; C = 43 pF. No Zbulk term was necessary. The interneedle capacitance obtained in the fit is of the same magnitude as the approximate value calculated above, but is necessarily smaller because of the short length of the actual needle electrodes. The same model was also used to obtain the excellent fits to the measured impedance spectra in Figure 1. In that case Ael = 1.9x106, nel = 0.72, Rel = 500 kΩ; Abulk = 5.0x107, nbulk = 0.80, Rbulk = 1.1 kΩ; Rhi-f = 3kΩ; C = 6 pF. Figure 4 presents the impedance spectrum for a necrotic newt tail. The model of Figure 3 was again used to obtain the excellent fits to the measured spectra. In this case Ael = 2.3x106, nel = 0.72, Rel = 11 MΩ; Abulk = 1.2x108, nbulk = 0.72, Rbulk = 26 kΩ; Rhi-f = 40 kΩ; C = 5.5 pF. The main difference between the parameters for the normal and necrotic tails is the large increase in the resistances. Several

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Log freq. (Hz) Fig. 4 Impedance Spectreum of a necrotic newt tail. Squares, ReZ; Circles, ImZ; Dashed lines indicate the fits provided by the circuit shown in Figure 3.

IV. DISCUSSION Tissue resistance generally decreases when cells are damaged or killed because the integrity of the cell membrane is reduced. Ions may enter the intercellular fluid, and the membrane provides less of a barrier for current transport. Freezing of apples [17] or chicken [18] significantly increases conductivity. Heating frog muscle [19], rat muscle [5] or apples [13] above about 42oC damages the membrane and also increases conductivity. Electrical Impedance Spectroscopy measurements made on a variety of damaged tissues show a decrease in resistivity following injury [20]. For this reason we are surprised by the large increase of resistance of the necrotic tissue compared to normal tail tissue. However, Martinsen et al. [21] observed an initial increase in fish muscle resistance as the muscle went into rigor following sacrifice, but then a subsequent decrease as relaxation followed. We intend to examine the increase in resistance which we observed in order to clarify its origin. We have identified an artifact in the measuring process due to the interneedle capacitance. Electrode capacitances are generally a concern for low frequencies at the tissue/electrode interface, but not at high frequencies where lead inductance may be more of an issue. We report this observation here so that other researchers may be aware of this potential problem for small tissue samples and needle

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Impedance Spectroscopy of Newt Tails

electrodes. Otherwise this dispersion might be mistakenly attributed to the tissue being studied.

REFERENCES 1.

Miklavcic D, Pavselj N and Hart F (2006) Electric Properties of Tissues. in "Wiley Encyclopedia of Biomedical Engineering", M. Akay, ed., Wiley, New York, Vol. 6, 3578-3589 2. Blad B, Wendel P, Jonsson and Lindstrom K (1999) An electrical impedance index to distinguish between normal and cancerous tissues. J Med Eng Tech 22: 1-5 3. van Kreel B K, Cox-Reyven N and Soeters P (1998) Determination of total body water by multifrequency bio-electric impedance: development of several models. Med Biol Eng Comput 36: 337-345 4. Schaefer M, Gross W, Ackemann J and Gebhard M M (2002) The complex dielectric spectrum of heart tissue during ischemia. Bioelectrochemistry 58: 171-180 5. McRae D A and Esrick M A (1993) Changes in electrical impedance of skeletal muscle measured during hyperthermia. Int J Hyperthermia 9: 247-261 6. Iten LE and Bryant S V (1973) Forelimb regeneration from different levels of amputation in the newt, Notophthalmus viridescens: Length, rate, and stages. Development Genes and Evolution 173: 263-282 7. Iten LE and Bryant S V (1976) Regeneration from different levels along the tail of the newt, Notophthalmus viridescens. Journal of Experimental Zoology 196: 293-306 8. Baranowitz S A, Maderson P F A and Connelly T G (1979) Lizard and newt tail regeneration: A quantitative study. Journal of Experimental Zoology 210: 17-38 9. Brockes J P and Kumar A (2005) Appendage regeneration in adult vertebrates and implications for regenerative medicine. Science 310: 1919-1923 10. Hart F X, Berner N J and McMillen R L (1999) Modelling the anisotropic electrical properties of skeletal muscle. Phys Med Biol 44: 413-421 11. Hart F X, Toll R B, Berner N J and Bennett N H (1996) The low-frequency dielectric properties of octopus arm muscle measured in vivo. Phys Med Biol 41: 2043-2052

193 12. Hart F X, Ismail U and Schmidt VA (2001) Comparison of the impedance spectra of frog gastrocnemius, octopus arm and crayfish tail muscles, measured in-vivo. Proc. XI Intl Conf on Electrical Bio-Impedance, Oslo, Norway, 2001. ICEBI; 321324. 13. Hart F X, Schmidt V, Ray L and Shrum E (1998) Measurement of the activation enthalpies for ionic conduction in apples. J Mat Sci 33: 3919-3925 14. Bao J-Z, Davis C C and Schmuckler R E (1992) Frequency domain impedance measurements of erythrocytes. Biophys J 61: 1427-1434 15. Bates J B, Lubben D, Dudney N J and Hart F X (1995) 5 Volt Plateau in LiMn2O4 Thin Films. J Electrochem. Soc 142: L149-L151 16. Wang B, Bates J B, Hart F X, Sales B C, Zuhr R A and Robertson J D (1996) Characterization of Thin-Film Rechargeable Lithium Batteries with Lithium Cobalt Oxide Cathodes. J Electrochem Soc 143: 3204-3213 17. Hart F X and Cole W H (1993) Dielectric properties of apples in the range 0.1 – 100 kHz. J Matl Sci 28: 621-631 18. Bodakian B and Hart F X (1994) The dielectric properties of meat. IEEE Trans Dielec & Elec Insul 1: 181-187 19. Hart F X, Davila-Moriel E E, Berner N J and McMillen R L (2005) Thermal hysteresis of bioelectrical impedance in frog gastrocnemius muscle, measured in-vivo. J Non-Crystalline Solids 351: 2929-2934 20. Heroux P and Bourdages M (1994) Monitoring living tissues by electrical impedance spectroscopy. Annals Biomed Engng 22: 328-337 21. Martinsen O G, Grimnes S and Mirtaheri P (1996) Electrical properties of fish muscle post mortem. Med Biol Eng Comput 34: Suppl 1, Part 2 187-188 Author: Institute: Street: City: Country: Email:

Francis X. Hart University of the South 735 University Avenue Sewanee USA [email protected]

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Inherently Synchronous Data Acquisition as a Platform for Bioimpedance Measurement G. Poola1 and J. Toomessoo2 1

Artec Group, and Dep. of Electronics, Tallinn University of Technology ,Tallinn, Estonia 2 Artec Design, IEEE graduate member, Tallinn, Estonia

Abstract— Direct sampling of carrier signal with dedicated Digital Signal Processor is today widely used as a method for bioimpedance measurement. Due to limits in sampling rates that general DSP can provide, and due to the fact that DSPs are not fully synchronous, it is difficult to guarantee synchronization between sampling and generated carrier. By replacing DSP with FPGA it is possible, not only to make inherently synchronous system, but also to create flexible platform for product development. Keywords— Synchronous data acquisition, bioimpedance, stimulus generation, FPGA, Ethernet.

I. INTRODUCTION Direct sampling of measurement signal used for electrical bioimpedance measurement is becoming the most widely used method today. Even more, in the last decade the bioimpedance has gained its popularity mostly due to using of the Digital Signal Processors (DSP), as in Figure.1., and other digital methods for acquiring bioimpedance data in a digital form, and processing it [1-3]. For accomplishing measurements of the bioimpedance in a wide frequency range quite a dedicated Digital Signal Processor (DSP) is to be used as the sampling rates that the general purpose DSPs can provide, are relatively limited. In addition, due to the fact that DSPs are not fully synchronous, it is very difficult to guarantee precise synchronization between sampling and generated excitation signal. Precise synchronization is needed to obtain exact timing of sampling of the picked-up voltage as a carrier of information about the bioimpedance of interest. Exact timing is needed for determination of the complex components of the bioimpedance, especially of the relatively smaller quadrature component in presence of the inphase component. Thus, an idea appeared to overcome these problems by replacing the general purpose DSP with a special signal processor accomplished on the basis of a field programmable gate array (FPGA). With this solution it becomes possible to build up an inherently synchronous Data Acquisition System, but also to create a flexible platform for development of a whole series of different products based on bioimpedance measurement.

Combining low noise digital signal synthesis, with high rate synchronous sampling, to avoid the shortcomings of standard DSP approaches in bioimpedance measurement and providing adaptable platform, for similar applications and research purposes, with reusable infrastructure, lowering time to market for new methods and overall cost of resulting products. There are multiple different approaches for this, but they all have basic common functions, that have to be solved for such a digital system to be built. I. Configuration and control II. Stimulus generation III. Data acquisition IV. Data processing V. Data transfer to host VI. Electrical isolation There are differences in data processing (the algorithms used), acquisition techniques (under sampling, synchronous sampling) and also in stimulus generation (single frequency, multi frequency). Most of the architectures can be reduced to described steps while some already follow them [1;4]. Some of these solutions are forced on the researches by technology. For example, standard DSP can not handle high frequency sampling, so under sampling has to be used in high frequency carrier demodulation.

Fig. 1 Standard DSP bioimpedance system

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Inherently Synchronous Data Acquisition as a Platform for Bioimpedance Measurement

II. PROPOSED APPROACH Field Programmable Gate Array (FPGA) based platform for research and product development is the best option when flexibility is needed. It also opens up new avenues for converting from FPGA based platform to Application Specific Integrated Circuit (ASIC) if large scale production of devices should arise. FPGA based design also enables the use of methods that are not available on standard DSP based systems. A. Synchronous sampling Synchronous sampling reduces processing complexity and avoids noise resulting from asynchronous sampling of generated stimulus signals [4,5]. Achieving completely synchronous sampling of generated stimulus signal is possible when the stimulus stays synchronized with the sampling pulse. This requires alternate approach for Direct Digital Synthesis (DDS) to generate inherently synchronous output. The approach contains changed angle accumulator so that the increments cause the accumulator to always reach exact equivalent of 90 degrees resulting the quarter period of signal to be synchronous to the system clock. For that purpose the design uses two parameters to change the output frequency. System clock divider (1) and angle increment (2) (1) (2) when the 90 degree equivalent point (3) is always (3) The sampling pulses for the picked-up carrier measurement are derived from these synchronization points allowing periodical sample collection from the exact same location on the stimulus signal. This makes it possible to collect samples over many stimulus signal periods. Furthermore FPGA based architecture makes it possible to use much higher sampling rates 100 Million Samples Per Second (MSPS). Higher sampling rates enable to collect substantially more data from multiple sources making it possible to detect even subtle changes in the carrier signal. This level of synchronous sampling can not be achieved with standard DSP designs as DSPs are not inherently synchronous. B. Direct digital synthesis Direct sine calculation is used to achieve the best possible signal to noise ratio, instead of Look Up Table (LUT),

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what is generally used in DDS removing the sine look up error from the digital domain. This reduces the need to post process or filter the signal further. The theoretical Signal-toNoise Ratio SNR (4) of a 16 bit signal is 96dB that comes form the quantization noise for 16 bits conversion assuming a uniform distribution of input signal values. (4) With direct sine calculation the SNR depends on precision of the calculation. Adding one bit to the calculation unit results a linear design size increase. In the case of using LUT adding one bit of precision will double the size of the LUT. Direct sine calculation algorithms have all inherent error. The calculation itself is either not long enough (polynomial algorithms) or is not very precise (approximation algorithms). The problem with approximation algorithms can be solved by calculating sine with higher precision and discarding part of the product, that contains the calculation error. This approach can deliver high resolution sine for DDS. III. RESULTS Developed bioimpedance data acquisition and stimulus generation platform based on Xilinx Spartan3 1000 FPGA enables synchronous signal generation in the Hz to Mhz range and is, in tests, capable to sample up to 200 MSPS, with two parallel Analog-to-Digital Converters (ADC). FPGA platform provides reusable infrastructure, with 100 Mbps Optical Ethernet connection, User Datagram Protocol (UDP)/Internet Protocol (IP) stack, configuration registers and dedicated control processor, consuming only 15% of the device resources. The Optical Ethernet serves as an isolation, form external noise, for medical applications. Providing driver free, optically isolated and high speed interface, to host based applications on any operating system, with network support. Other components in the synchronous bioimpedance measurement design consume about 50% of the FPGA device (12% sine calculation, 38% sampling control and filters). SNR of the signal generator digital output in hardware simulations is around 84dB for 16 bit output 1.5 MHz signal. The 84dB is not a design barrier, it comes form the choice of internal precision and could be changed for better or worse. The sine calculation is at the moment capable of 80 million separate 16 bit sine values per second, from 24 bit input angle, making it suitable for concurrent multifrequency applications[4].

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Fig. 4 Using current prototyping platform

for ASIC design

conversions, that are possible, enables the use of time multiplexing the sine block for multifrequency signal generation. For 10 MSPS DAC up to eight parallel sines can be calculated, while using Spartan3 integrated multipliers to form the final output signal. IV. APPLICATION

Fig. 2 Current application for the platform 0 -10

1024p DFT 16bit f=1.5625 MHz

Implementing the reusable platform, with FPGA, enables rapid research and product development. Reusing existing infrastructure enables researchers to concentrate on developing new methods at lower cost. Described platform makes it possible to proceed with ASIC prototyping and ASIC based portable device platform design.

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Fig. 3 1024 point DFT of 16 bit signal f=1.5625 Figure 2. displays current solution on the platform intended for multi frequency - multi source bioimpedance analyzer. Host PC based solution makes it possible to use existing proprietary software tools to design and test different sampling methods, processing algorithms and visualization ways for bioimpedance data. The graph in Figure 3. is simulation output of proposed signal generator. It has achieved SNR of 84dB with 32 bit internal calculation precision. The high rate of angle to sine

A. Prototyping platform The prototyping platform will enable design and production of hand held scale ASIC solutions for bioimpedance measurement. This prototyping phase is possible with Advanced RISC Machine (ARM) based System on Chip (SoC) providing Media Independent Interface (MII) and LCD control for display integration. These system level interfaces are common on many network oriented SoCs, providing fast and seamless integration of existing components to a prototyping platform. Second task in prototyping phase is software porting to embedded platform host PC. Figure 4. displays one of the possible ways ARM based SoC can be connected to existing FPGA over MII interface. The PC based software would have to be integrated in to ARM firmware based on GNU Linux operating system. Providing a clear Application Programming Interface (API) for future software modules defining the product functionality.

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Inherently Synchronous Data Acquisition as a Platform for Bioimpedance Measurement

B. Bioimpedance SoC Integrating existing platform FPGA with ARM processor in one SoC will bring further scale-down and reduce power consumption. Bioimpedance measurement SoC would make wide range of products possible. Different signal generation configurations (different ranges or sets of frequencies) and different interpretations of bioimpedance data would allow the SoC to be used for different purposes. Figure 5 shows the integration of the bioimpedance SoC (BioSoC) into a portable product platform. This platform would enable different bioimpedance solutions to be developed, by developing in fact only the software modules. That makes the platform functionality useful in achieving the set goals, and making the development faster and cheaper. V. CONCLUSIONS The designed synchronous sampling platform enables to: collect, process and transport very large amounts of sampling data. This makes it possible to demodulate extremely small bio-modulations form very high frequency carrier signals reaching 5 MHz. The fully synchronous calculation based DDS design enables the use of high resolution digital-to-analog converters, making it possible to produce low noise carrier signals.

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The phase by phase approach in design of bioimpedance measurement devices, enables to commercialize the result of every intermediate platform, lowering the risk and development cost of final target platform. This approach also enables research results, in the field of bioimpedance measurement, to reach market faster in the form of final products. The research and development cost reduction and reduction in overall Bill Of Materials (BOM), will hopefully reduce the cost of bioimpedance based medical equipment, making them widely available for: hospital patient monitoring, tissue analysis, medical organ diagnostics, sport diagnostics and self health monitoring devices.

ACKNOWLEDGMENT The authors thank prof. Mart Min and Toomas Parve for their help and support. The work has been partly financed by the Estonian Science Foundation under the grants 5892, 5897, and 7212, and is connected with the project 2.2 of the ELIKO Competence Center in Electronics-, Info- and Communication Technologies, Tallinn, Estonia.

REFERENCES 1.

2. 3.

4.

5.

Gordon R, Land R, Min M et al (2005) A virtual system for simultaneous multi-frequency measurement of electrical bioimpedance. Int. Journal of Bioelectromagnetism vol. 7, 2, (Special Issue: Proc. 2005 BEMNFSI Conf., Minneapolis, MN, USA, May 12-15, 2005), Omnipress, pp 243-246. Dudykevych T, Gersing E, Thiel F and Hellige G (2001) Impedance analyser module for EIT and spectroscopy using undersampling. Physiological Measurement, No 22, 2001, IoP Publ. Ltd, UK, pp 19-24. Hartov A, Mazzarese R A, Reiss F R et al (2000) A multichannel continuously selectable multifrequency electrical impedance spectroscopy measurement system. IEEE Trans. on Biomedical Engineering, vol. 47. No.1, January 2000, pp 49-58. Min M, Parve T, Annus P, Paavle T. (2006) A method of synchronous sampling in multifrequency bioimpedance measurements,: Proc. of the IEEE Instrumentation and Measurement Technology Conf. IMTC2006, Sorrento, Italy, 24-27 April 2006, pp 1699-1703. Min M, Parve T, Poola G (2005) Methods of multi-frequency bioimpedance measurement in implantable and wearable devices. Proc. 3rd European Medical & Biological Engineering Conf. EMBEC 2005, Nov. 20-25, 2005, Prague, Czech Republic, 6 p. (CD) Author: Gustav Poola

Fig. 5 SoC based bioimpedance measurement device

Institute: Street: City: Country: Email:

Dep. of Electronics, Tallinn University of Technology Ehitajate tee 5 Tallinn Estonia [email protected]

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Parameter Optimization in Voltage Pulse Plethysmography M. Melinscak

Abstract— Measurement optimization is being studied when short voltage pulses are stimulating bio-tissue and the transient process is sampled in order to measure the tissue volume changes. The measurement sensitivity depends on the ratio of the sampling instant to the time constant of the transient process, (T/τ) and on the ratio of the current sensing resistance to the resistance of the electrode-skin interface (R0/RSX). With variations of R0/RSX and T/τ the sensitivity changes from negative to positive values, while it equals zero for certain R0/RSX and T/τ ratios. The sensitivity is greater when positive while than negative but it depends on T/τ and R0/RSX. For negative sensitivity, T and R0 can be chosen to maximize the sensitivity and minimize its variations.

uo[v]

Polytechnic of Karlovac, Karlovac, Croatia

Keywords— voltage pulse plethysmography, electrode tissue interface, tissue resistance variation

Fig. 2 The output voltage for normal and deep breathing respiration [1]

normal breathing

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I. INTRODUCTION

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Plethysmography is a method of measuring volume changes in bio-tissue. Impedance plethysmography is most often applied in measuring volume changes due to blood pulsation (Fig. 1) or respiration (Fig. 2) whereby the biotissue resistance changes. In this study the voltage pulse plethysmography is being examined. Short voltage pulses activate the bio-tissue and the characteristics of the transient process are measured (Fig. 3, Fig. 4).

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Parameter Optimization in Voltage Pulse Plethysmography

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The measurement sensitivity depends on the voltage sampling instant, T and resistance of the current sampling resistor. Dependence of the sensitivity with y=R0/RSX as a parameter is in Fig. 7 and with x=T/τ as a parameter is in Fig. 8. δR=-10 % δR=20%

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The electrode-skin interface can be modeled with a fiveelement circuit that can be simplified to a three-element circuit due to the circuit symmetry (Fig. 5) [2]. The impedance of the electrode-skin interface is frequency dependent [3]-[5] and at high frequencies [2], [6] which are applied in pulse plethysmography, can be replaced by a series RC-circuit (Fig. 5), where the variable resistance, ΔRSX is added in series with RSX simulating the resistance change due to the change of the bio-tissue volume. This resistance change is rather small, typically 0.1-0.5 % [6], [7].

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Fig. 5 Equivalent circuits for electrode-skin interface [2]

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and y=1

⎞ ⎟ ⎟⎟ ⎠

The voltage variation with constant current sampling resistance R0 and ΔRSX/RSX as a parameter is shown in Fig. 6. It is evident that all curves intersect at one point, that is, the measurement sensitivity equals zero for certain T/τ ratio with fixed R0/RSX as a parameter. The relative measurement sensitivity is defined as the variation of the sampled voltage variation du (t=T) normalized to the impulse amplitude U0, relative to the relative change of the bio-tissue resistance RSX: (2)

y=1

y=1.5

y=2

6,00

8,00

0,25 0,2

S

0,15 0,1 0,05 0 -0,05 0,00

2,00

4,00

10,00

T/t

Fig. 7 Measurement sensitivity S with y=R0/RSX as a parameter x=1

x=3

x=5

x=7

x=9

0,12 0,1 0,08 0,06

S

0,04 0,02 0 -0,02 -0,04 0

x

⎛ x ⎞ − S = (du / U 0 ) /( dR / R) = − ⋅⎜ − 1⎟⎟ ⋅ e 1+ y 2 ⎜1+ y ( y + 1) ⎝ ⎠

y=0.5

0,3

(1)

where y = R0 / RSX , δR = ΔR SX /RSX , x = T / τ , τ = R SX C X

y

6

Fig. 6 Voltage dependence u(t)/U0 with constant sampling resistance R0

Fig.3 shows the block diagram of the voltage pulse plethysmography. One electrode of the circuit is grounded while voltage sample is taken on the other electrode. The analysis is performed for this implementation. The other possible implementation is when the current sampling resistor R0 is grounded. The analysis for this implementation is very similar describing in fact the same transient process. A simple analysis results in the expression for voltage u(t): ⎛ y ⎜ ⋅e u (t ) = U 0 ⋅ ⎜1 − ⎜ 1 + y + δR ⎝

4

T/t

II. OPTIMIZATION METHODS

x − 1+ y +δR

2

0,5

1

1,5

2

R0/RSX

Fig. 8 Measurement sensitivity S with x=T/τ as a parameter

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M. Melinscak

Dependence of the sensitivity on R0/RSX ratio is defined S R = (dS ) /[dy / y ] = as: = −

[y

3

y ( y + 1) 5

⋅e

−

x 1+ y

(4)

⋅

(

]

)

+ y 2 ⋅ (1 − 3 ⋅ x ) + y ⋅ x 2 − 2 ⋅ x − 1 + x − 1

and is shown in Fig. 11. The curve SR for a certain T/τ passes through zero and for this value R0/RSX the sensitivity curve S has a minimum.

Fig. 9 Surface plot of measurement sensitivity S depending on T/τ and R0/RSX.

In Fig. 9 is a surface plot of measurement sensitivity S depending on T/τ and R0/RSX. Dependence of the sensitivity on the relative variation of the sampling instant dT/T is defined as: (3)

and is shown in Fig. 10. The curve ST for a certain R0/RSX passes through zero and for this value of T/τ the sensitivity curve S has a minimum. y=0.5 0,06 0,04 0,02 0,00 -0,02 ST -0,04 -0,06 -0,08 -0,10 -0,12 -0,14 0,00

2,00

y=1

4,00

y=1.5

6,00

y=2

8,00

10,00

T/τ

Fig. 10 Dependence of the measurement sensitivity on voltage sampling instant, ST with y=R0/RSX as a parameter

x=1

0,50

x=3

x=5

1,00

x=7

x=9

1,50

2,00

R0/RSX

Fig. 11 Dependence of the measurement sensitivity on R0/RSX ratio, SR

x

− y⋅x 1+ y [ ( ) ] ST = (dS ) /( dT / T ) = − ⋅ 2 ⋅ 1 + y − x ⋅ e ( y + 1) 4

0,07 0,06 0,05 0,04 0,03 SR 0,02 0,01 0,00 -0,01 -0,02 -0,03 0,00

with x=T/τ as a parameter

III. CONCLUSION The measurement reliability can be improved in view of maximizing the measurement sensitivity and minimizing the sensitivity variations regarding the variations of the sampling instant, T and the variations of the current sampling resistance to the bio-tissue resistance ratio, R0/RSX. The positive sensitivity for a small T/τ ratio is considerably greater than the negative sensitivity but it varies considerably with a change in T/τ and R0/RSX ratios. For the negative sensitivity T and R0 can be chosen so that at the same time the absolute sensitivity is maximum (the minimum of the sensitivity curve) whereby the variations with T and R0 are minimized.

REFERENCES 1. Melinscak M, Santic A. (2007) Features of voltage pulse plethysmography, The fifth IASTED International Conf. on Biomedical Engineering, Innsbruck (accepted for presentation) 2. Santic A, Kovacic D, Bilas V. (1999) Some new aspects of electrical impedance and pulse plethysmography, European

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Parameter Optimization in Voltage Pulse Plethysmography

3. 4. 5. 6.

Medical and Biological Engineering Conf. EMBEC, Vienna, pp. 114-115 Franks W, Schenker W, Schmutz P et al. (2005) Impedance characterization and modeling of electrodes for biomedical applications, IEEE Trans Biomed Eng 52(7):1295-1302 Klyuev AL, Rotenbergb ZA, Batrakov VV. (2005) Impedance of a passive iron electrode in a solution containing a reducing agent, Russ J Electrochem 41(1):87-90 Geddes LA. (1972) Electrodes and the measurement of bioelectric events. John Wiley & Sons Santic A. (1990) Pulse plethysmography in the blood pressure measurement at the finger, 6th IMEKO Conf. on Measurement in Clinical Medicine, Sopron, pp. 29-31

201 7. Santic A, Stritof T, Bilas V. (1998) Plethysmography measurements using short current pulses with low duty-cycle, 20th Ann. Int. Conf. of the IEEE EMBS, Hong Kong, pp. 1889-1892 Address of the corresponding author: Author: Martina Melinscak Institute: Street: City: Country: Email:

Polytechnic of Karlovac Ivan Mestrovic 10 Karlovac Croatia [email protected]

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Separation of electroporated and non-electroporated cells by means of dielectrophoresis J. Oblak1, D. Krizaj2, S. Amon2, A. Macek-Lebar2 and D. Miklavcic2 1

2

Institute for Rehabilitation, Ljubljana, Slovenia University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia

Abstract— By exposing cells to high voltage electric pulses, cells’ membrane permeability increases significantly. Phenomenon is known as electroporation and is widely used in biotechnology, biology and medicine, as a way of introducing into a cell molecules which otherwise are deprived of membrane transport mechanisms. Besides cell membrane permeability, the cells’ geometrical and electrical properties change significantly due to electroporation. These changes have a huge impact on dielectrophoretic force, which could allow us to separate the electroporated and non-electroporated cells. Usually, a test whether a cell is electroporated or not is performed by exposing cells to a dye. After such test cells are most often not useful for further use. For this reason cell separation based on dielectrophoretic force could be very useful, because cells are not destroyed or changed due to dielectrophoresis. In this study we report the results of an attempt to separate the electroporated and non-electroporated cells by means of dielectrophoresis. In several experiments we managed to separate the electroporated and non-electroporated cells suspended in a medium with conductivity 0.174 S/m by exposing them to a non-uniform electric field at a frequency of 2 MHz. Because experimental results did not match theoretical predictions entirely we presume that cell membrane permittivity decreases after electroporation for at least ten times.

method for manipulation and separation of microscopic particles is dielectrophoresis [5]. Dielectrophoresis (DEP) is a phenomenon in which a force is exerted on a polarisable particle, e.g., a biological cell, when it is subjected to a nonuniform electric field [6]. In addition to cell manipulation and separation, the dielectrophoresis spectra may be also used to derive electric properties of the cell [7]. In this study we report on measurements on dielectrophoresis behavior of electroporated and non-electroporated cells.

Keywords— electroporation, dielectrophoresis, separation, membrane permittivity

where R is the radius of the cell, εmed is the absolute permittivity of the suspending medium, E is the electric field acting on a cell and Re[ƒCM] is the real part of the ClausiusMossotti factor [6]. The Re[ƒCM] describes MaxwellWagner relaxation and is given by:

I. INTRODUCTION In biotechnology, biology and medicine, it is sometimes important to be able to introduce desired extracellular molecules that are normally cell membrane impermeant. Electroporation is a widely-used technique for delivering a large variety of impermeable molecules, such as drugs [1] and genes [2] into cells, both in vitro and in vivo. This is a phenomenon during which exposure of a cell to an electric field results in a significant increase in its membrane permeability [3]. Normally we test if cell is successfully electroporated or not by exposing cells to a dye (propidium iodide, trypan blue, lucifer yellow...) [4]. Further the cell is destroyed and, as such, can not be used. In many cases, researchers would prefer a more convenient method which would allow separation of electroporated and nonelectroporated cells from the cell suspension. A very useful

II. MATERIALS AND METHODS The dielectrophoretic force is generated through the interaction of the non-uniform electric field and the induced electric dipole of the cell. The direction and magnitude of the dielectrophoretic force depends on the dielectric properties of the cell and the suspending medium. If we assume that a biological cell is a spherical particle the dielectrophoretic force is defined as:

FDEP = 2πR 3ε med Re[ƒ CM ]∇ E 2 ,

ƒ CM =

ε 'cel −ε 'med ε 'cel +2ε 'med

,

(1)

(2)

where ε'cel and ε'med are the complex permittivities of the cell and medium and are defined as: ε' = ε–j(σ/2πf), where ε is permittivity, σ is conductivity and f is the frequency of the electric field. Therefore, the Re[ƒCM] is frequency dependent. The most important frequency of Re[ƒCM] spectra is the crossover frequency f0, at which Re[ƒCM] changes its sign, either from the negative to the positive or vice versa (dielectrophoresis crossover). Aside from the fact that after the electroporation the cell membrane becomes more permeable, the cells’ geometrical [8] and electrical properties [9] change, which has a large impact on the magnitude and the

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sign of Re[ƒCM]. Taking into account that the electroporated cells has a different Re[ƒCM] spectra than the nonelectroporated one, we expected that it would be possible to separate cells at a specific frequency of the electric field.

era (Visicam, Visitron Systems, Germany). Each experiment was repeated at least three times.

A. Experiments

According to Eq. (1), the frequency dependent Re[ƒCM] has the most significant impact on cell motion caused by dielectrophoresis. If Re[ƒCM] of a cell is negative, the cell is subjected to negative dielectrophoresis and moves away from the high-field intensity. However, if Re[ƒCM] of a cell is positive, the cell is subjected to positive dielectrophoresis and moves toward the high-field intensity. Re[ƒCM] was calculated from Eq. (2) for the electroporated and nonelectroporated cells suspend in medium M1. Another crucial parameter in Eq. (1) is the gradient of the of the electric field. Several structures are suitable for development of large non-homogeneities of electric field [6]. In this work we used castellated electrode structures. Areas of high- and low-field intensities were determined numerically, using finite element modeling software FEMLAB [www.comsol.com].

The conductivity of the medium has a significant role on the dielectrophoretic force [6] and electroporation [10]. For this reason, medium M1 with conductivity σM1 = 0.174 S/m was prepared. For preparation of medium M1 the following ingredients were dissolved in 200 ml distilled water: 272.2 mg KH2PO4, 348.4 mg K2HPO4, 19.02 mg MgCl2 and 17115 mg sucrose. The conductivity of prepared medium was measured by a Conductometer MA 5950 (Metrel, Slovenia). All experiments were carried out at room temperature (23°C). Mouse melanoma cell line, B16F1, was grown for four days in Eagle's minimum essential medium supplemented with 10% fetal bovine serum (Sigma-Aldrich Chemie GmbH, Deisenhofen, Germany). To meet the required concentration for electroporation, t.i. 2 × 107 cells/ml [10], the cells were diluted with medium M1. A 50 μl drop of diluted cell suspension (containing 106 cells) was placed between two parallel plate stainless steel electrodes spaced 2 mm apart and exposed to electric pulses. A train of eight rectangular pulses amplitude: 230 V, duration: 100 μs, repetition frequency: 1 Hz, was generated and monitored with the Cliniporator (IGEA, Carpi, Italy). Parameters of electric pulses for reversible electroporation in medium M1 were chosen according to Ref. [10]. The dielectrophoretic manipulation of cells was performed by a castellated electrode array, which was fabricated on a 500 μm thick wafer of Pyrex glass using microtechnology processing steps. The microelectrode structures were fabricated in the Laboratory of Microsensor Structures, Faculty of Electrical Engineering, University of Ljubljana, Slovenia. The fabrication of the module with microelectrode array was described in detail previously [11]. The cell suspension was diluted with a medium M1, since the most appropriate concentration to observe cell motion under the microscope is 2 × 106 cells ml. After the electroporation cells were left for 5 min to swell and reseal before being examined by dielectrophoresis. Sinusoidal signals of magnitude 7 Vpp were applied to the pair of electrodes over the frequency range 5 kHz - 50 MHz using a function generator 33250A (Agilent, USA). Cells exposed to generated non-uniform electric field were observed under the transmission microscope (Zeiss 200, Axiovert, Jena, Germany). The frequency of the electric field was varied and the images were recorded with a cam-

B. Numerical calculations

III. RESULTS In Fig. 1, calculated Re[ƒCM] and f0 of the electroporated and non-electroporated cells in medium M1 are illustrated. As we can see, cells are exposed to a negative dielectrophoresis at low frequencies and to a positive dielectrophoresis at high frequencies of the electric field. The calculated crossover frequency f0 is around 0.4 MHz for the nonelectroporated cells and around 0.2 MHz for the electroporated cells.

Fig. 1 Re[ƒCM] and f0 of the electroporated and non-electroporated cells in medium M1, calculated according to the electric properties of cells and suspending medium.

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J. Oblak, D. Krizaj, S. Amon, A. Macek-Lebar and D. Miklavcic

The distribution of the non-uniform electric field generated with the castellated electrode array at applied voltage between the electrodes is shown in Fig. 2. If cells are exposed to a positive dielectrophoresis, they will move toward the edges of the electrodes where the high-field intensity is generated. Exposing cells to a negative dielectrophoresis would direct the cell motion towards the middle of electrodes where low-field intensity areas are. Experiments revealed intense cell motion by exposing cells to the dielectrophoresis in medium M1. At low frequencies of applied electric field, cells are exposed to negative dielectrophoresis. At a frequency of about 0.4 MHz, the non-electroporated cells started to move toward the edges of the electrodes. The movement of the electroporated cells however was different from the theoretical predictions (Fig. 1). Electroporated cells were exposed to negative dielectrophoresis up to the frequency 10 MHz. Therefore, crossover frequencies of the non-electroporated (0.4 MHz) and electroporated (10 MHz) cells differ for at least an order of magnitude. We repeated each experiment at least three times by increasing and decreasing the frequency of the applied signal and the results of experiments were repeatable. IV. DISCUSSION By comparing theoretical and experimental results we conclude that the calculated f0 of the non-electroporated cells matches the experimentally obtained crossover frequency. At low frequencies the non-electroporated cells move toward the areas of the low-field intensity and at high frequencies they move toward the areas of the high-field intensity. Experimentally determined f0 of the nonelectroporated cells is between 0.3 MHz and 0.5 MHz, which corresponds well to the calculated crossover frequency f0 = 0.4 MHz. The calculated f 0 = 0.2 MHz of the electroporated cells did not match the experimentally determined

Fig. 2 Plot of the numerically calculated non-uniform electric field generated with the castellated electrode array. Dark shades represent areas of high-field intensity, whereas bright shades those of low-field intensity.

Fig. 3 Calculated Re[ƒCM] and f0 of the electroporated and nonelectroporated cells in medium M1, considering 10 to 20 times lower permeability of cell membrane after electroporation. crossover frequency. The electroporated cells are exposed to negative dielectrophoresis up to a frequency of about 10 MHz. According to theoretical predictions we expected that the crossover frequency f 0 of the electroporated cells is lower than that of the nonelectroporated ones. However, the experiments revealed just the opposite. The theoretical predictions for the electroporated cells were based on studies [8, 9] and on the single-shell model of a spherical cell [12]. Therefore, one possibility is that we did not succeed to describe a biological cell with the single-shell model, because the model was too simple. A more likely option is that at least one property of the electroporated cell from studies [8, 9] is not correct. At the electroporation, significant changes appear particularly on the cell membrane. There were many studies investigating the electric conductivity of the membrane, however, there are not many studies on a cell membrane permittivity after electroporation. It is interesting that the cell membrane permittivity in particular has a significant role on f 0 . If we require the calculated f 0 of the electroporated cells suspended in medium M1 (see Fig. 3) to correspond to the experimental results, the cell membrane permittivity must have been reduced after the electroporation. Considering that the non-electroporated and electroporated cells have different f0, the cells could be separated with dielectrophoresis at a frequency of about 2 MHz, see Fig. 3. In several experiments cells suspended in medium M1 were exposed to dielectrophoresis at frequency of 2 MHz. On the basis of cell arrangement around the microelectrode structures it was easy to determine if the cells were electroporated or not, see Fig. 4.

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Fig. 4 Separation of electroporated and non-electroporated cells at a frequency of 2 MHz. Non-electroporated cells are exposed to positive dielectrophoresis, therefore they move toward the edges of the microelectrode. On the other side, electroporated cells move toward the middle of the two adjacent electrodes because they are exposed to positive dielectrophoresis.

V. CONCLUSION The aim of our study was to investigate the separation possibilities of the non-electroporated and electroporated cells by means of dielectrophoresis. It was obvious from the experimental results that the behavior of the non-electroporated and electroporated cells exposed to dielectrophoresis is different. Since the experimental results did not match the theoretical predictions we believe that a cell membrane permittivity decreases after electroporation for at least ten times of its value before the electroporation. By exposing the cells suspended in medium M1 to the non-uniform electric field at frequency of 2 MHz, we succeed to separate the non-electroporated and electroporated by means of dielectrophoresis.

ACKNOWLEDGEMENTS Dielectrophoresis experiments could not have been preformed without preparation of the microelectrode structures made by Darko Lombardo and the members of Laboratory of Microsensor Structures, Faculty of Electrical Engineering, University of Ljubljana, Slovenia. This research was supported by Slovenian Research Agency.

3.

Teissie J, Rols MP (1993) An experimental evaluation of the critical potential difference inducing cell membrane electropermeabilization. Biophys J 65: 409-413 4. Kotnik T, Macek-Lebar A, Miklavcic D, Mir LM (2000) Evaluation of cell membrane electropermeabilization by means of nonpermeant cytotoxic agent. Biotechniques 28:921-926 5. Lynch PT, Davey MR (1996) Electrical Manipulation of Cells. Chapman & Hall, New York 6. Morgan H, Green NG (2003) AC Electrokinetics: colloids and nanoparticles. Microtechnologies and Microsystems series, Institute of Physics Publishing, Bristol 7. Marszalek P, Zielinsky JJ, Fikus M, Tsong TY (1991) Determination of electric parameters of cell membranes by a dielectrophoresis method. Biophys J 59:982-987 8. Pavlin M, Leben V, Miklavcic D (2006) Electroporation in dense cell suspension—Theoretical and experimental analysis of ion diffusion and cell permeabilization. Biochim Biophys Acta 1770:12-23 9. Pavlin M, Kanduser M, Rebersek M, Pucihar G, Hart FX, Magjarevic R, Miklavcic D (2005) Effect of Cell Electroporation on the Conductivity of a Cell Suspension. Biophys J 88:4378-4390 10. Pucihar G, Kotnik T, Kanduser M, Miklavcic D (2001) The influence of medium conductivity on electropermeabilization and survival of cells in vitro. Bioelectrochemistry 54:107-115 11. Lombardo D, Vrtacnik D, Krizaj D (2004) Microstructures for manipulation of micro and submicron particles using dielectrophoresis. Proc 40th International Conference on Microelectronics, Devices and Materials MIDEM 2004: 163-168 12. Pauly VH, Schwan HP (1959) Uber die impedanz einer suspension von kugelformigen teilchen mit einer schale. Z Naturforsch 14b:125-131 Author: Jakob Oblak

REFERENCES 1.

2.

Marty M, Sersa G, Garbay JR, Gehl J, Collins CG, Snoj M, Billard V, Geertsen PF, Larkin JO, Miklavcic D, Pavlovic I, Paulin-Kosir SM, Cemazar M, Morsli N, Soden DM, Rudolf Z, Robert C, O’Sullivan GC, Mir LM (2006) Elect Electrochemotherapy – An easy, highly effective and safe treatment of cutaneous and subcutaneous metastases: Results of ESOPE study. Eur J Cancer Suppl 4:3-13 Neumann E, Kakorin S, Toensing K (1999) Fundamentals of electroporative delivery of drugs and genes., Bioelectrochemistry and Bioenergetics 48:3-16

Institute: Street: City: Country: Email:

Institute for Rehabilitation Linhartova 51 Ljubljana Slovenia [email protected]

__________________________________________ IFMBE Proceedings Vol. 16 ___________________________________________

Conducting Implant in Low Frequency Electromagnetic Field B. Valic1,2, P. Gajsek2 and D. Miklavcic3 1

E-NET Okolje, Ljubljana, Slovenia Institute of Non-ionizing Radiation, Ljubljana, Slovenia 3 University of Ljubljana/Faculty of Electrical Engineering, Ljubljana, Slovenia 2

Abstract— Based on the medical images, a finite element model of the human body with intramedular nail in the femur was prepared. The model was exposed to low frequency (50 Hz) electromagnetic field with the intensity of ICNIRP reference levels for general public. Calculated current density distribution inside the model was compared to ICNIRP basic restrictions for general public. From the results it can be seen hat the implant increases current density in the region where it is in contact with soft tissue. The increase is significant. ICNIRP basic restrictions are exceeded in limited volume of the tissue in spite of compliance with ICNIRP reference levels for general public. On the other hand, the region, where significant increase in the current density is observed, is limited to few cubic centimeters only. Keywords— numerical modeling, finite elements, dosimetry, implants, low frequency exposure

I. INTRODUCTION With the increasing number of implants in modern medicine an important question has to be clarified: Could ICNIRP basic restriction levels [1] be exceeded when implant is present in the body in spite of fulfilling ICNIRP reference levels. In the recent years there were some papers published about the problems of electromagnetic field distribution in the human body with implant. For example in [2] they calculated SAR distribution and temperature change around a metallic plate in the head of a RF (100 to 3000 MHz) exposed worker. Virtanen et al. [3] calculated SAR enhancements due to ring and rod shaped metallic implants at mobile frequencies (900 and 1800 MHz). In 2006 Virtanen published also a review paper [4] about the interactions between RF electromagnetic fields and passive implants. But all of the papers are dealing with RF electromagnetic field, whereas for low frequency electromagnetic field there is no publication. To determine the influence of an implant on electromagnetic field distribution inside a human we used numerical modeling to calculate electromagnetic field distribution in a human with intramedular nail in the low frequency (50 Hz) electromagnetic field. We included intramedular nail, used to fix broken cancelous bones, because it is one of the long-

est implants used. Beside intramedular nail, also bones of the right leg were included: femur, patella, fibula and tibia. Material properties were derived from the literature. By appropriate boundary conditions electromagnetic field intensity at the reference levels for general public was generated inside the model. For comparison of the result and the influence of the implant on electromagnetic field distribution also models without the implant were calculated. II. MATERIALS AND METHODS A. Geometry of the model The geometry of the model is based on the female images form the Visible Human Data Set (VHDS), available from National Library of Medicine, National Institutes of Health, USA. They are taken in the interval of 0.33 mm with 0.33 mm resolution. Each of 5186 images consists of 2048 × 1216 pixels with 24 bit color depth (Fig. 1). Numerical modeling was performed with Finite Element Method program package Comsol Multiphysics, Comsol, Sweden. Numerical modeling stated with defining which tissues we will include in the model. The more tissues we include, more detailed the solution will be but this means also that mesh will become denser and more complicated. We included only soft tissue for the whole model and bones in the region near the implant only. Pro-

Fig. 1 An example of the images from the VHDS. The shown one (avf2300.raw) is taken in the region below the knees.

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Conducting Implant in Low Frequency Electromagnetic Field

gram package requirements lead to seven soft tissue objects: head, upper and lower part of the torso, two arms and two legs. The build-up of each object started with selection of images, starting with every 150th image (the distance between the images 5 cm). After we manually cleared the surrounding of desired object (tissue type) in every image, a custom written algorithm replaced the border of the object with polyline with predefined number of nodes. Finally nodes of all polylines were connected together to obtain three dimensional object. After the first iteration we analyzed the object and included additional images in the area where the geometry was not accurate enough. The geometry of the femur is shown in Fig. 2. The geometry of the intramedular nail was based on Xray images of a woman with broken right femur. Images were taken at University Medical Centre, Ljubljana, Slovenia. Before we were able to include the nail in the model we had to orient X-ray images with respect to the objects, based on VHDS images. The intramedular nail inside the femur is shown in Fig. 2 (right). The calculation region was defined by the block with the dimension 5 × 5 × 8 m. Because of large scale geometry (the ratio between the smallest and the largest dimension in the geometry) automatic mesh algorithm implemented in Comsol Multiphysics was unable to generate mesh. By splitting the geometry in two geometries it was possible to decrease the scale. As shown in Fig. 3 instead of one geometry we have two geometries: big geometry is a block with dimension 5 × 5 × 8 m with a cut out in the lower centre part, and small geometry, with the same dimension as cut out: 0.5 × 0.5 × 2 m. The geometry of the human body is situated in the small geometry. In both geometries, after minor correction of human body geometry and fine tuned parameters of mesh generation algorithm we were able to

Fig. 2 Geometry of the right femur (left) is based on 15 images. On the

219

Fig. 3 In lower part of the big geometry (left) there is visible cut out, where by identity boundary conditions small geometry (right) is inserted.

generate the mesh. Final mesh consisted of 40486 elements in small geometry and 988 elements in big geometry. Quasi-static electromagnetic application mode was used, valid when the largest dimension in the geometry is one order of magnitude smaller than the wavelength of electromagnetic field. To obtain the electromagnetic field with the values of ICNIRP reference levels for general public at 50 Hz (5 kVm-1 for electric field and 0,1 mT for magnetic flux density) required boundary conditions were defined. Since all materials in the model have the same permeability (Table 1) and current density in the model is very low (we can neglect induced magnetic field), the magnetic field distribution inside the model is homogeneous. This means that it is necessary to calculate magnetic field distribution only in the small geometry and not in both geometries, so we set magnetic boundary conditions on the boundaries of the small geometry to -0.1 μT in x direction. For electric field we applied 40 kV on the upper boundary of the big geometry and grounded the lower boundary (all other four boundaries were set to insulation) resulting in electric field 5 kVm-1 in z direction. Using special boudary conditions, called identity boundary conditions on the corresponding boundaries in big and small geometry it is possible to connect both geometries to behave as one when the model is calculated. Dielectric properties of the tissues in the model were taken from the data reported by Gabriel [5, 6]. Because of data dispersion at 50 Hz in the literature we made parameterization for tissue specific conductivity and permittivity. It was shown that only the value of tissue specific conductivity is important.

right side there is intramedular nail inserted in the femur.

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Table 1 Material properties of objects included in the model Material soft tissue bone implant

σ (Sm-1) 0,2 – 0,4 0,005 – 0,009 4,032 ×106

εr 6

7

10 – 10 103 – 104 1

μr

source

1 1 1

5, 6 5, 6 Comsol Multiph.

To compare the results and determine the influence of the intramedular nail on electromagnetic field distribution inside the body we calculated also a model without the intramedular nail. III. RESULTS Using Finite element method we evaluated the influence of the intramedular nail in the femur on the distribution of low frequency (50 Hz) electromagnetic field and compared the results of the model with and without the intramedular nail to ICNIRP basic restrictions for general public. From the Fig. 4 it can be seen that human body disturbs electric field. The boundary conditions define the unperturbed electric field strength with the value of ICNIRP reference level for general public (5 kVm-1). Because electric field strength inside the human body is low in comparison to the surrounding area electric field strength is increased in the region near and above the head. The values are up to 20 kVm-1, which is 4 times higher than in unperturbed field. At low frequencies (up to 100 kHz) ICNIRP basic restrictions limits the value of current density. At 50 Hz the limit for head and trunk is 2 mAm-2.

Fig. 4 Electric field distribution in the big (left) and small (right) geometry is altered by human body: it is very low inside the human body (blue), but quite high near and above the head. Colorbar in Vm-1 covers the range from 0 to 6 kVm-1, a little above ICNIRP reference level for general public (5 kVm-1). The transparent area is where electric field is higher than 6 kVm-1.

Fig. 5 Current density is shown in the vertical crossection through the centre of the human body and 5, 10 and 15 cm to the right. Colorbar in mAm-2 covers the range from 0 to basic restriction for ICNIRP general public exposure (2 mAm-2). From the Fig. 5 it can be seen that current density inside the head and torso is lower than ICNIRP basic restriction for general public. In the legs below the knees current density is higher than basic restriction (transparent). However this is irrelevant since the basic restriction for current density is valid only in head and trunk. The current density inside the bones is low due to their low conductivity. In the last cross section at the right the top of the intramedular nail partially out of the bone is shown and it is in touch with soft tissue. Just above the top of the intramedular nail there is a small area in the soft tissue, where current density is higher than basic restriction (white). More detailed influence of the implant is shown in Fig. 6. First and third row presents data for the model with the implant and the other two for the model without it. As shown on the left of the figure we varied soft tissue specific conductivity from 0.2 Sm-1in the first and second row to 0.4 Sm-1in the third and fourth. In the first column, the crossection 0.6 m above the ground is shown, which is just above the knees. In the implanted leg current density is lower than in the other leg, similar is seen the second column showing crossection 0.7 m above ground. In the fourth column the results just above the intramedular nail are shown, where ICNIRP basic restriction for general public is exceeded. But only 2 cm higher (column 5) there are only minor differences between the results of the models with implant and without them. This shows that the influence of the implant is limited to the region, where the implant is situated and that this influence fades quickly with distance. The maximum 10 g averaged current density in the model is located just above the implant. It is 7 mA/m2, 10 times higher than in the model without the implant. As already mentioned, ICNIRP basic restriction for current density for general public is 2 mA/m2.

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Fig. 6 Current density in crossections parallel to the ground 0.6 m (just above the knees), 0.7, 0.8, 0.88 (just above the intramedular nail), 0.9 (just above the femur) and 1 m above the ground is shown for the model with (first and third row) and without (second and fourth row) the implant for two different specific conductivity of soft tissue (0.2 Sm-1in the first and second row and 0.4 Sm-1in the third and fourth row). The colorbar is the same as in previous figure.

IV. DISCUSSION AND CONCLUSIONS Using numerical modeling we determined the influence of intramedular nail on electromagnetic field distribution inside a human at low frequency electromagnetic field. In the region, where intramedular nail is in touch with soft tissue, there is significant increase in current density – it is 10 times higher than in the model without the implant. This leads to exceeding the ICNIRP basic restrictions on current density for general public. Except in the leg, where intramedular nail is implanted there is no observable difference in current density distribution in other parts of the body. We defined the geometry of the model from two different medical images (VHDS for human body and X-ray for implant). It would be better to build the geometry of the model from anatomical images of patients with implant, but whole body 3D images of implanted patients were not in the reach of us. Low frequency exposure was chosen because there is no paper available dealing with low frequencies, whereas for RF exposure there are few presented. A low number of papers in this field, and most of them are less than few years old, indicates that future work on this model is necessary to obtain more valuable data. For example, we should use different exposures (direction, strength, frequency) or include different implants in the model.

ACKNOWLEDGMENT Thanks to asist. mag. Anže Kristan at University Medical Centre, Ljubljana, Slovenia for patient images.

REFERENCES 1. 2.

3. 4. 5. 6.

ICNIRP (1998) Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz). Health Phys 74:494-522 McIntosh RL, Anderson V, McKenzie RJ. (2005) A numerical evaluation of SAR distribution and temperature change around a metallic plate in the head of a RF exposed Worker. Bioelectromag 26:377-388 Virtanen H, Huttunen J, Toropainen A, Lappalainene R. (2005) Interaciton of mobile phones with superficial passive implants. Phys Med Biol 50:2689-2700 Virtanen H, Keshvari J, Lappalainen R. (2006) Interaction of Radio Frequency Electromagnetic Fields and Passive Metallic Implants – A Brief Review. Bioelectromag 27:431-439 Gabriel C, Gabriel S, Corthout E. (1996a) The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol 41:2231-2249 Gabriel C, Law RW, Gabriel S. (1996b) The dielectric properties of biological tissues: II Measuerements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 41:2251-2269 Author: Dr. Blaz Valic Institute: Street: City: Country: Email:

E-NET Okolje Kajuhova 17 Ljubljana Slovenia [email protected]

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Effect of Modulated 450 MHz Microwave on HumanEEG at Different Field Power Densities R. Tomson, H. Hinrikus, M. Bachmann, J. Lass, and V. Tuulik Department of Biomedical Engineering, Technomedicum of Tallinn University of Technology, Tallinn, Estonia Abstract⎯ The experiments on the effect of modulated microwaves on human EEG were carried out on two different groups of 14 and 7 healthy volunteers exposed to 450 MHz microwave radiation modulated at 40 and 1000 Hz frequencies. The field power densities at the scalp were 0.16 mW/cm 2 for the first and 0.9 mW/cm 2 for the second group. The EEG analysis performed for individuals showed that increase in the EEG rhythm energy in both groups was comparable: up to 40% at lower and up to 30% at higher level of the field power density. Microwave caused statistically significant changes in the EEG rhythms energy for 20% of subjects in the first and for 14% of subjects in the second group. Our results suggested that effect of microwave on EEG didn’t enlarged with increase of the applied power density.

field power densities. In this paper we concentrated on evaluation of the EMF effect at different field power densities. A hypothesis is that changes caused by microwave in EEG and number of subjects affected increase at higher level of microwave field power density. The relative changes in EEG rhythms energy were selected as a quantitative measure to evaluate the effect. Two identical modulation frequencies 40 and 1000 Hz were applied at two different levels of microwave power.

Keywords⎯ EMF effect, nonthermal effect, EEG analysis.

Microwave exposure conditions were the same for all subjects in a group, except field power densities. The 450 MHz microwave radiation was 100% pulse modulated at 40 and 1000 Hz frequencies (duty cycle 50%). The 1 W or 10 W output power was guided by a coaxial cable to the 13 cm antenna, located 10 cm from the left side of the head. Estimated by the measured calibration curves, the field power densities at the skin were 0.16 mW/cm2 for the first and 0.9 mW/cm2 for the second group. The calculated SAR values were about 0.35 W/kg for the first [1] and 2 W/kg for the second group.

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I. INTRODUCTION The increasing applications of telecommunication devices roused problem of possible effects of the radio frequency electromagnetic field (EMF) on human brain physiology. High-level microwave radiation causes heating of tissues and a microwave thermal effect. Effect of a microwave at levels lower than thermal limit on human nervous system have become a subject of discussions and the reports of possible non-thermal EMF effects are often contradictory. The difficulties in independent repeating of the experimental results cause doubt in these effects and mechanisms behind the effects are still unclear. During resent years our studies have been focused on the effect of modulated low-level microwaves on human EEG theta, alpha and beta rhythms and mental behaviour [1-6]. Based on our previous studies, high variability between EEG signals of individuals and very different sensitivity to microwave among subjects did not allowed to reveal statistically significant effect of microwave for a group but only for individuals. During all experiments we used a field power density 0.16 mW/cm2 and estimated SAR values 0.35 W/kg were lower the thermal effect limit. A rate of persons affected by microwave has been between 12 and 30%. Our expectation was that the microwave effect and rate of sensitivity would be higher at higher P

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II. METHODS A. Microwave exposure

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B. Subjects The experiments with different modulation frequencies were carried out on two different groups of healthy volunteers. The first group included 14 persons (aged 2124, 8 male and 6 female); the second group 7 persons (aged 19-21, 3 male and 4 female). All the subjects selected were without any medical or psychiatric disorders. Persons who declared tiredness or sleepiness before the experiment were excluded. All subjects passed the experimental protocol with exposure and sham. During each double blind test session, the exposed and sham-exposed subjects were randomly assigned. The subjects were not informed of their exposure, however, they were aware of the possibility of being exposed. Computer randomly assigned also the succession of modulation frequencies.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 210–213, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

Effect of Modulated 450 MHz Microwave on HumanEEG at Different Field Power Densities

The experiments were conducted with understanding and written consent of each participant. The study was conducted in accordance with the Declaration of Helsinki and has formally approved by the local Medical Research Ethics Committee. C. Recording protocols and equipment The experimental protocols were identical for two groups except differences in field power (Fig. 1). 1 cycle 2 min Ref 1min CI

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Fig. 1 Time-schedule of the recording protocol during ten cycles of microwave exposure at fixed modulation frequency: first half-period of the cycle passive without microwave (Ref) and second half-period active with microwave (MW); signals for analysis were selected inside comparison intervals (CI) during first 30 s of the half-periods.

Firstly, the reference EEG was recorded during 1 minute. After that, the microwave exposure, modulated at first modulation frequency, was applied during 1 minute. Relaxation pause after the stimulation by microwaves (as reference for the following cycle) was also 1 minute. The procedure of the reference-exposure cycle was repeated ten times at fixed modulation frequency. During ten cycles of the microwave exposure, the modulation frequency was always the same. Second, the same procedure of 10 exposure cycles was applied at second modulation frequency. The procedure of this step was identical to the previous one, except that the modulation frequency was different. EEG recordings were continuously performed during all experimental protocol (40 min). The selection of the 40 or 1000 Hz as first or second modulation frequency was randomly assigned. The second protocol (sham) included the same steps (EEG recording during 40 min), except that the microwave power was switched off. The Cadwell Easy II EEG measurement equipment was used for the EEG recordings. The EEG was recorded using 9 electrodes, which were placed on the subject's head according to the international 10-20-electrode position classification system. The channels for analysis were chosen to cover the entire head: frontal FP1, FP2; temporal T3, T4; parietal P5, P4; occipital O1, O2; and the reference electrode Cz. The EEG recordings were stored on a computer with a 400 Hz sampling frequency.

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D. Data analysis At first, the energies of four basic EEG rhythm frequencies theta (4-7 Hz), alpha (8-13 Hz), beta1 (1520Hz) and beta2 (21-38 Hz) were extracted from the total EEG signal (0.5 - 48 Hz) by filtering. The elliptic bandstop filters with 50 dB attenuation in the stop-band were used. Further analysis followed the method, described in our previous studies [1, 3]. The EEG energies of the exposure cycle half-periods with and without microwave were compared. The first 30 s of recovery and exposure halfperiods of the cycle were selected as comparison interval. The relative change of the EEG energy for a cycle was calculated as S=(s1/s2 – 1) x100 %, B

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where s1 and s2 were the average energies inside the comparison intervals with and without exposure respectively. The parameter Sm as an average value of S over 10 cycles of exposure at the same modulation frequency for a subject was selected as a quantitative measure of the microwave effect. Processing of the signal was performed in the LabVIEW programming and signalprocessing environment. Statistical evaluation was performed according to the “zero hypothesis”. For sham recordings, signal segments with and without exposure are completely equivalent. The natural variance of the energies of the segments σ could be obtained as the mean of their squared differences for sham 2 −2 is an f-distributed recordings. The quantity x = (S m ) σ random quantity and respective p-values are obtained by means of the cumulative f-distribution. Post hoc analysis the modified Bonferroni correction was applied with a 0.05 confidence level. B

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III. RESULTS Values of the parameter Sm were calculated for all subjects at two modulation frequencies in theta, alpha, beta1 and beta2 rhythms and F1-F2, T3-T4, P3-P4 and O1-O2 channels for exposed and sham recordings. Calculated values of the parameter Sm in T channels as average for the whole groups are presented in Fig. 2. Main trend is decrease of the EEG energy with microwave exposure in theta and increase in alpha, beta1 and beta2 rhythm frequencies. Maximal increase reaches 17% in the first and 12% in the second group. Main trend is decrease of the EEG energy with microwave exposure in theta and increase in alpha, beta1 and beta2 B

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rhythm frequencies. Maximal increase reaches 17% in the first and 12% in the second group. Changes in the EEG beta1 rhythm energy for individuals in T channels are presented in Fig.3. In the case of first group there are 3 subjects with obvious increase (30-40%) in the EEG beta rhythm energy at 40 Hz modulation frequency. In the second group two persons demonstrate increase in EEG beta rhythm energy (15-30%) also at 40 Hz modulation frequency. No obvious increase revealed at 1000 Hz modulation frequency. The EEG beta1 rhythm was affected in majority only by 40 Hz modulation frequency. The results demonstrate that in majority of experiments the microwave exposure causes increase of the EEG energy level. Statistical analysis for the whole group was performed for average values of changes for 10 cycles (parameter Sm). The analysis didn’t reveal significant differences between sham and exposed results for the groups. Statistical analysis for individual subjects was performed for parameters S calculated for every exposure cycle before averaging over ten cycles of exposure at a fixed modulation frequency. Summary of statistical analysis for individuals as identification numbers of subjects with significant changes is presented in Table 1.

Tab. 1. Identification numbers of subjects with statistically significant differences between sham and exposed results after Bonferroni correction (p<0.05)

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Absence of statistically significant differences between calculated measures for individuals in the case of sham recordings confirm that the changes are really introduced by microwave. Most sensitive to exposure was EEG beta rhythm – all statistically significant changes took place in EEG beta rhythms.

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IV. DISCUSSION

ACKNOWLEDGEMENT

Changes in the EEG energy caused by microwave at field power densities lower and higher than the thermal limit are comparable. The values of relative changes are even slightly higher (30-40%) at lower than at higher microwave power (15-30%). Number of subjects significantly affected is 3 at lower and 1 at higher power. Rate of sensitivity is 20% for the first and 14% for the second group. Sensitivity to microwave didn’t increase with rising applied field power density. Microwave modulated at 40 Hz causes more notable changes and for more number of subjects than modulated at 1000 Hz microwave. Dependence of microwave effect on modulation frequency has been indicated also in our previous studies at 7, 14 and 21 Hz modulation frequencies [3, 7]. Our experiments didn’t reveal stronger effect of the microwave exposure at higher power density. The results suggest that effect caused by modulated microwave doesn’t have a linear dependence on the level of applied radiation power. Non-monotonic dependence of the effect on microwave radiation level has been reported also by other researchers [8]. Numbers of subjects affected by microwave as nonspecific stressor 14-20% are not low compared to the rate of multiple chemical sensitivity (exposures to multiple chemically unrelated compounds at doses far below those established to cause harmful effects occurrence) estimated to be between 2 and 10 % in the general population [9].

These studies have been supported by the Estonian Science Foundation grant No 6632.

REFERENCES 1. 2.

3.

4.

5.

6. 7.

V. CONCLUSIONS Results of our experimental study do not confirm the raised hypothesis about stronger microwave effect at higher field power density. Experimental data showed that 450 MHz microwave modulated at 40 and 1000 Hz caused comparable changes in the EEG at levels of the field power densities lower and higher than thermal limit. Increase in applied microwave power didn’t result in increase of the effect or number of affected individuals. Microwave caused statistically significant changes in the EEG rhythms energy for 20% of subjects at lower and for 14% of subjects at higher field level. Our results suggest that the microwave exposure causes significant changes in human EEG, affects larger part of population than chemically unrelated compounds. The mechanism of the findings is not clear and the effects needs further investigation.

8.

9.

Hinrikus, H., Parts, M., Lass, J., Tuulik, V. (2004): Changes in Human EEG Caused by Low Level Modulated Microwave Stimulation. Bioelectromagnetics 25: 431-440. Bachmann, M., Kalda, J., Lass, J., Tuulik, V., Sakki, M., Hinrikus, H. (2005): Non-linear analysis of the electroencephalogram for detecting effects of low-level electromagnetic fields. Medical & Biological Engineering & Computing, 43:142-149. Lass, J., Hinrikus, H., Bachmann, M., Tuulik, V. Microwave radiation has modulation frequency dependent stimulating effect on human EEG rhythms. Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, USA, 2004, pp. 4225-4228. Bachmann, M., Säkki, M., Kalda, J., Lass, J., Tuulik, V., Hinrikus, H. (2005): Effect of 450 MHz microwave modulated with 217 Hz on human EEG in rest. The Environmentalist, 25: 165-171. Lass, J., Tuulik, V., Ferenets, R., Riisalo, R. Hinrikus, H. (2002) Effects of 7Hz-modulated 450 MHz electromagnetic radiation on human performance in visual memory tasks. International Journal of Radiation Biology, 78: 937-944. Rodina A., Lass J., Riipulk J., Bachmann T., Hinrikus H. (2005) Study of effects of low level microwave field by method of face masking. Bioelectromagnetics, 26: 571-577. Hinrikus, H. Bachmann, M., Tomson R., Lass, J. (2005): Nonthermal effect of microwave radiation on human brain. The Environmentalist, 25: 187-194. Gapejev, A., Chmeris, N., Fesenko, Y. and Khramov, R., (1994) Resonance effects of a low-intensity modulated extremely high frequency field. Change in the motor activity of the unicellular protozoa Paramecium Caudatum, Biophysics, 39: 73-84. Cullen, M.R. (1987) Workers with multiple chemical sensitivities. Occupational medicine: state of the art reviews. Philadelphia: Hanley & Belfus, Inc.; 2: 655-661. Author: Hiie Hinrikus Institute: Department of Biomedical Engineering, Technomedicum of the Tallinn University of Technology Street: 5 Ehitajate Rd City: Tallinn Country: Estonia Email: [email protected]

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EMF Monitoring Campaign in Slovenian Communes B. Valic1, 2, J. Jancar1 and P. Gajsek1 1

Institute of Non-ionizing Radiation, Ljubljana, Slovenia 2 E-NET Okolje, Ljubljana, Slovenia

Abstract— To give the Slovenian communes and their inhabitants the possibility to obtain information about electromagnetic fields in their neighborhood, Forum EMS – an independent project aimed to inform general public about electromagnetic fields and their biological effects – started monitoring campaign in 2005. In communes expressed the interest, remote monitoring station was installed for one week. The value of electric field intensity was stored every minute 24 hours a day and. For each location all collected data were evaluated and presented to the interested public as an article in commune bulletin and on internet, where the data for all locations are available. In last two years more than 35 communes participated in this campaign. The monitoring campaign showed that typical electromagnetic field exposure due to GSM base stations in urban area is low. Maximum values reach 2 % of reference level for I. region of Slovenian legislation, which is 0.2 % of ICNIRP reference level for general public. Due to the vicinity of radio and TV broadcasting tower in one case, instead of GSM probe, wide band probe was used. In this case, measured electric field was 40 % of reference level for I. region of Slovenian legislation (4 % of ICNIRP reference level for general public). Keywords— Monitoring, Electromagnetic fields, Dosimetry.

Sevnica, Trebnje, Trebnje, Vojnik and Domžale; and in 2006: Puconci; Šempeter-Vrtojba, Nova Gorica, Izola, Piran, Koper, Semič, Kočevje, Šk. Loka, Bled, Cerklje, Šenčur, Naklo, Litija, Trbovlje, Hrastnik, Žalec, Šoštanj, Slovenj Gradec, Črnomelj nad Kranj. II. MATERIALS AND METHODS The remote monitoring system used in this campaign is intended for permanent monitoring of wide frequency band EMF at desired location. It consists of remote monitoring station and central unit. The station measures the value of EMF in predefined intervals and stores them temporarily. Once or twice a day all data are transmitted to the central unit through bi-directional GSM link. The central unit stores data and is used to configure parameters of the station. Detailed description of the system is given in next chapter. A. Remote monitoring station

I. INTRODUCTION

Remote monitoring station PMM 8055 (PMM, Italy) [1] is autonomous automatic monitoring system. It consists of measuring and controlling electronics, probe, GSM modem, rechargeable batteries and case with solar cells.

With the increase of electromagnetic field (EMF) sources, it is becoming more and more important to constantly monitor EMF in living environment. Forum EMS – project in Slovenia aimed to objectively and independently inform people about EMF and their biological effects – decided to start an EMF monitoring campaign in Slovenian communes in year 2005. All Slovene communes were invited to join the campaign, 20 of them participated in monitoring every year. During the year EMF remote monitoring station is located for one or two weeks in those communes. After the monitoring, for every location a report is prepared, where average and maximum values of electric field strength are present. To inform the public about the results, the article in commune bulletin is prepared and data are available also on the internet. Till now nearly 40 communes take part in this campaign: in 2005: Maribor, Šmartno ob Paki, Brezovica, Zreče, Grosuplje, Novo mesto, Ljubljana, Tržič, Velenje, Kamnik,

Figure 1 Remote monitoring station.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 234–237, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

EMF Monitoring Campaign in Slovenian Communes

Thanks to solar cells and rechargeable batteries station could be used indoor and outdoor independent of power source. The control software allows fully automatic system operation with wide variety of user defined settings. It monitors the field strength over a wide frequency band with regard to the probe. We use three different probes. Two are narrow band E field probes with measuring range between 0.03 and 30 V/m for two different frequency bands: GSM (925 – 960 MHz) and UMTS (2110 – 2170 MHz). One is wide band E field probe (100 kHz do 3 GHz) which is less sensitive (0.3 – 300 V/m). Measurement uncertainty of probes is given in Table 1. Table 1 Measurement uncertainty Probe type GSM Wideband UMTS

Measurement range [V/m] 0.03 – 30 0.3 – 300 0.03 – 30

Frequency range [MHz] 925 – 960 10 – 2500 2110 – 2170

Expanded measure. uncertainty ± 1,8 dB ± 2,6 dB ± 1,8 dB

The field strength is monitored continuously with a custom time-averaging period, according to Slovenian legislation [2] we use 6 minutes period. The measurements are stored at predefined time periods, we use 1 minute. Through bi-directional GSM link results are automatically downloaded to central unit periodically every 12 hours. During the activity of GSM connection, all measurements are marked; since it is necessary to exclude them form the results. The GSM modem allows data transfer to the central unit as well as modifications of all parameters of the station. There are two ways how to change parameters. First this could be done through web application running on the server, which is part of the central unit. Changed parameters are sent from the server to the station through GSM link. Second this could be done by SMS from mobile phone. Using special commands it is possible to change parameters of the station or even receive SMS from the station with different data, for example current measured field value.

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• an internet server, where the parameters of the remote monitoring station could be modified and where all data are available to the user; • dedicated E-Smoguard software to manage the application (requires Linux). There are two main tasks for internet server. First is to allow surveying stored data (shown in Figure 2) and the second is to allow modifications of remote monitoring station parameters using Graphical User Interface (shown in Figure 3). In most of the communes, GSM probe was used, since GSM base stations are the most important sources of EMF. Only in the case of Puconci, where radio and TV broadcasting tower is located in the middle of the village, broad band field probe was used to evaluate EMF due to the radio and TV tower.

Figure 2 An example of monitoring results as available on internet. Green line presents electric field strength (V/m) whereas the red one presents the reference level according to Slovenian legislation [2], which is, for GSM frequency, 12.9 V/m. On axis x date and time of measurement is shown.

B. Central unit The central unit E-Smoguard 1.02 (Clampco Sistemi, Italy) consists of [3]: • two GSM modems (one for SMS and one for data transfer); • a terminal management unit which receives the data transmitted from remote monitoring station and converts it into a protocol accepted by the internet server;

Figure 3 GUI for modifications of remote monitoring station parameters.

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III. RESULTS Results of EMF monitoring in Slovenian communes show that the values of electromagnetic fields are well below ICNIRP reference level for general public [4] and reference level for I. region of Slovenian legislation [2], which is 0.01

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10 times lower. The highest maximum value was measured in Domžale, where the maximum was 1.66 V/m, whereas the maximum average value 1.05 V/m was measured in Maribor. The value 1.66 V/m presents only 1.7 % of reference level for I. region of Slovenian legislation. The results for Puconci, marked with * are discussed on next page. 1.00

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Figure 4 Maximum and average results. GSM probe was used, which measures signals between 925 MHz and 960 MHz. Green line presents reference level for I. region of Slovenian legislation at 950 MHz; the red line presents ICNIRP reference level for general public at same frequency. In Puconci (marked with *) wideband probe was used because of the proximity of radio and TV broadcasting tower. Wideband probe measured EMF between 10 and 2500 MHz. Monitoring locations are listed in chronological order: from Maribor to Domžale in year 2005, from Puconci to Kranj in year 2006.

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EMF Monitoring Campaign in Slovenian Communes

In commune Puconci, the station was located in the village Pečarovci where radio and TV broadcasting tower is situated. Because of this, wideband probe was used to measure also EMF at radio and TV frequencies. Measured values were higher in Pečarovci than at other monitored locations: 5.1 V/m for maximum value and 3.8 V/m for average value. Reference level is different than in the case of GSM system because of different frequency: according to Slovenian legislation [2], it is 8.6 V/m and according to ICINIRP it is 28 V/m for general public. Taking into account this reference level, EMF reaches 35 % of reference level for I. region of Slovenian legislation and 3.5 % of ICNIRP reference level for general public. Based on the results of monitoring campaign in years 2005 and 2006 we can conclude that: • base stations do not present significant EMF source which should exceed current reference levels (Slovenian or international); • EMF intensities due to base stations are low on all monitored locations. On average they are below 1 % of reference level for I. region of Slovenian legislation [2]; • the highest measured EMF value due to base stations was 1.7 % of reference level for I. region of Slovenian legislation, measured in Domžale;

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• the highest average EMF value due to base stations was below 1 % of reference level for I. region of Slovenian legislation, measured in Maribor.

ACKNOWLEDGMENT This work was support by Forum EMS – project in Slovenia aimed to objectively and independently inform people about EMF and their biological effects.

REFERENCES 1. 2. 3. 4.

PMM 8055S Remotely operated station for monitroing electromagnetic fields form 5 Hz up to 40 GHz (2002) at http://www.pmm.it Uredba o elektromagnetnem sevanju v naravnem in zviljenjskem okolju. UL RS, 70/1996 E-Smoguard at http://www.e-smoguard.net ICNIRP (1998) Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz). Health Phys 74:494-522 Author: Blaz Valic Institute: Street: City: Country: Email:

Institute of Non-ionizing radiation Pohorskega bataljona 215 Ljubljana Slovenia [email protected]

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Measurements of background electromagnetic fields in human environment T. Trcek1, 3, B. Valic2, 3 and P. Gajsek3 1

University of Ljubljana/Faculty of Electrical Engineering, Ljubljana, Slovenia 2 E-NET Okolje, Ljubljana, Slovenia 3 Institute of Non-ionizing Radiation, Ljubljana, Slovenia

Abstract— An extensive measurement campaign was carried in Slovenia to evaluate background electromagnetic field intensity in different living environments. As electromagnetic field meter personal exposure meter Antennessa DSP 090 with analysis software for data processing and analysis was upgraded with commercial GPS receiver and custom made data logger unit to save geographical data of the measurements. Using this measuring system, mounted on a car roof or bicicle, eight locations across Slovenia were measured: cities Ljubljana and Maribor, part of city Kranj, part of city Koper with nearby villages Labor, Hrvatini and Tinjan and village Čelje near Ilirska Bistrica. The measured frequency spectrum was limited to GSM, DCS, UMTS, FM and TV signals. From the results it is clear that typical electromagnetic field exposure is low. Maximum values merely reach 3 % of reference levels for I. region of Slovenian legislation, which is 0,3 % of ICNIRP reference levels for general public. The only exception is Tinjan, where radio tower is located. Measurements done in the vicinity of this radio tower exceeded 5 V/m, which is upper detection limit of measuring system. The results of this measurement campaign will be presented on interactive map, open to general public through internet, where everyone will be able to obtain the measured values of every single measurement. Keywords— Antennessa DSP 090, Electromagnetic fields, Dosimetry

I. INTRODUCTION In modern lifestyle we are exposed to many different environmental factors: physical, chemical and biological factors from environment, which have great impact on our quality of life. At the same time public wants to have the information about quality of living environments. Both these factors stimulate different measurement campaign, inbetween also of background electromagnetic fields. Measuring background electromagnetic field (EMF) is an effective way to determine the overall exposure of general public to EMF. At the Institute for Non-ionizing radiation we used personal dosimeter with commercial name Antennessa DSP 090 for this purpose [1]. It is an example of selective instrument for measuring personal exposure to EMF through a longer time period. To expand the functionality of the personal dosimeter we connected it to the cus-

tom made data logger with GPS receiver. It allowed us to measure background EMF in human environment to determine the real exposure of general public to EMF in different living environments in Slovenia. Final goal of this research was to represent measurements as an array of points on an interactive map. The color of the points is an indicator of the real value of measured EMF, which is obtained by clicking on the point. In the future, the results will be available on internet. II. MATERIALS AND METHODS For EMF measuring specific measuring systems and methods are needed. One of such systems for selective EMF exposure measurement is Antennessa DSP 090. Figure 1 shows selective EMF measurement with Antennessa DSP 090, where contribution for each component of the EMF spectrum is shown. Personal exposure meter used as a measurement system for measuring background of EMF in human environment needs to be upgraded with GPS module and data logger unit to save the geographical coordinates of the measurements. Whole measurement system includes dosimeter Antennessa DSP 090, commercial GPS receiving module and custom made logger module used for GPS data storing and managing GPS module. Part of measurement system is also software installed on personal computer, handling measured data. All system components are shown in figure 2.

Figure 1: Selective EMF measurement with DSP 090

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Measurements of background electromagnetic fields in human environment

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A. Antennessa DSP 090 Antennessa DSP 090 is personal exposure meter for personal EMF. Measurement is focused on FM, TV and wireless communications as mobile phones and their base stations. These signals are main players and contribute the major part of EMF in urban environment. Measurement is selective within each frequency band. Main characteristics of Antennessa DSP 090 are: • • • • • • • • • •

registering EM signal 24 hour per day in adjustable intervals; insensibility to interferences with user activity; 40dB dynamic range with sensitivity 0,05-5V/m; isotropy; FM, TV, GSM, DCS and UMTS frequency range; distinction between base and subscriber station; adjustable sampling interval from 3 to 255 seconds; memory autonomy for 7000 measurement records; few days battery autonomy; small size and weight (450g).

B. GPS module GPS model consists from two parts. First part is a commercial GPS receiving module for receiving satellite signal and determining geographical coordinates. Second part is data logging and managing unit, used for mange GPS receiving module and logging GPS data. This unit was developed according to our requirements. Our basic requirements were: • • • • • •

memory autonomy comparable to DSP 090; data should be stored permanently (flash card); battery autonomy comparable to DSP 090; geographical coordinates from GPS should be converted to Gauss-Krueger coordinates; DSOP 090 LED synchronization pulse should be used to trigger pulse for data logging; supported connectivity with PC.

C. Measurements and data processing Prior to starting measurements the locations for measurements were defined. To obtain representative sample it is necessary to cover enough population as well as to include different living environments and EMF sources. Including large cities we included enough population and different EMF sources. But measurements in cities only are not representative enough and rural areas should be included too. In rural areas it is appropriate to include areas with at least one strong source of EMF.

Figure 2: Measurement system Considering all mentioned criterion we chosen following places for our measurement: Ljubljana, Maribor, part of Kranj, part of Koper with nearby villages: Labor, Hrvatini and Tinjan and Čelje near Ilirska Bistrica. The most part of measurements was realized by car with measurement system mounted on roof carriers. Some parts of Ljubljana with prohibited car traffic were measured by bicycle. During measurements occasional transfer of recorded data to computer was necessary due to the safety reasons. Data from dosimeter are written in a .mes format recognized by Antennessa software. Software allows exporting data to standard MS Excel format. Coordinates from data logger unit are stored in .txt format and must be combined with Excel data after transfer to computer. After combining, data without pairs value-coordinate were eliminated. It was also necessary to filter all measurements that are too close. We decided to filter out all measurements that are less then 20 meters apart. Program code for filtering was done by MS Visual Basic. It first calculates distance from one measurement to each other and then eliminates all measurements that are closer then 20 meters to comparable measurement. In principle each measurement has to be compared to each measurement, therefore number of comparisons rises with the square of measurements. For instance, in Ljubljana where more then 12000 measurements were acquired, 144 million of comparisons are needed. Calculation time was approximately half an hour. After filtering, measurement points were drawn into graph for fast survey. For Ljubljana, graph is shown in figure 3.

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T. Trcek, B. Valic and P. Gajsek

106000

104000

102000

100000

98000 458000

460000

462000

464000

466000

468000

Figure 3: Graphically presented filtered measurement points

The accuracy of filtered measurement points was verified on The Environmental Agency, Ministry of the Environment and Spatial Planning of the Republic of Slovenia. Coordinates obtained from GPS device always have some deviation, which is normally less than 1 m. In the case of inappropriate position of visible satellites deviation arises. Several measured points with obviously wrong position were displaced to the most credible position; those with doubtful position were deleted. An example of filtered and corrected measurements is shown in figure 4. When multiple frequency and/or source exposure is present Slovenian legislation [2] directs that all measurement results must be converted from V/m to percent of maximum values defined. Safety index SI is calculated using equation: Figure 4: Measurement points represented on map

SI=∑ (Ei/Emi)2,

(1)

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Measurements of background electromagnetic fields in human environment

where SI is safety index in percents, Ei is measured value of frequency component and Emi is maximum allowed value of frequency component. Maximum allowed value Emi is frequency dependent and has two values at each frequency. Slovenian legislation [2] differentiates the area between I. region area and II. region area. II. region area is where normal EMF exposure is allowed and is the same as ICNIRP reference levels for general public [3]. I. region area is where safety is increased, which means that allowed EMF exposure is decreased for factor 10. For measured frequency bands the values of Emi in V/m are shown in table 1.

were measured with values of 0.97 V/m, or 0.57% of I. region reference levels [2]. First value was measured in Studenci, precisely on Valvasorjeva street near base station. Second value was also measured in Studenci near base station on Koresova street. Like in Ljubljana, average values for Maribor show that GSM system is the greatest contributor. Average value of GSM signal in Maribor is 0.1 V/m, or 0.006% of I. area region reference levels. Other average values are at lower detection limit of measuring system (0.05 V/m) and are therefore not point of our interest. IV. CONCLUSION

III. RESULTS Measuring system consisting from Antennessa DSP 090, GPS receiving module and data logger was used to measure background EMF in human environment at 8 different locations across Slovenia. Totally almost 20000 measurements were captured. For each measurement following data were stored: EMF value for six different frequency ranges, coordinates, date and time. After the filtration (locations closer than 20 meters were excluded) 12059 measurements remained. For majority of locations the most important EMF source is GSM system with FM signals close. For Ljubljana maximum GSM signal was measured in Ljubljana-Tomacevo, where electric field strength was 2.28 V/m, or 3.1 % of I. region area reference levels [2]. Close to this was maximum FM signal, measured at Ljubljana Castle. Nearby there is radio diffusion system located and measured electric field strength was 1.57 V/m or 3.3 % of I. region area reference levels. Average values show that the most part of overall EMF exposure is contributed by GSM system. In Ljubljana average value of GSM signal is 0.127 V/m, or merely 0.01% of I. region area reference level. The results in Maribor are similar to those in Ljubljana. The value of maximum FM signal is 0.42 V/m or 0.42% of I. region area reference levels [2] and was measured nearby radio diffusion system in Tezno on Nikova street. In the frequency range of GSM signals two equivalent maximums

From the measurements it is clear that overall EMF exposure in human environment is low. Maximum values hardly riches 3% of I. region reference levels of Slovenian legislation [2], and are almost hundred times lower than allowed. Comparing results of different frequency ranges show that the major contributor to overall EMF exposure is GSM system. Only exception is Tinjan, where measurements were done in the vicinity of radio diffusion system. As expected for this location very high values of EMF were measured. Values were greater then 5 V/m, which is upper detection limit of measuring system. Our measurements also shows that exposure to EMF in largest cities are greater then in smaller cities or in rural areas. Beside measurements of additional locations the future work will focus on determining the number of people exposed to certain EMF values rather than the area with these EMF values.

ACKNOWLEDGMENT This work was partially support by Ministry of the Environment and Spatial Planning of the Republic of Slovenia.

REFERENCES 1. 2. 3.

Table 1: Maximum allowed value of EMF in V/m frequency\area FM TV 3 TV 4 & 5 GSM DCS UMTS

I. region area 8.6 8.6 9.3 12.9 18.2 19.0

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II. region area 27.5 27.5 29.7 41.1 58.1 61.4

Antennesa at at http//www.antennessa.com Uredba o elektromagnetnem sevanju v naravnem in žviljenjskem okolju. UL RS, 70/1996 ICNIRP (1998) Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz). Health Phys 74:494-522 Author: Blaz Valic Institute: Street: City: Country: Email:

Institute of Non-ionizing radiation Pohorskega bataljona 215 Ljubljana Slovenia [email protected]

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Numerical Assessment of Induced Current Densities for Pregnant Women Exposed to 50 Hz Electromagnetic Field A. Zupanic1, B. Valic2 and D. Miklavcic1 1

University of Ljubljana/Faculty of Electrical Engineering, Laboratory of Biocybernetics, Ljubljana, Slovenia 2 E-NET Okolje, Ljubljana, Slovenia

Abstract— A simple, yet anatomically realistic model consisting of five different tissue types was built to evaluate the effects of the low-frequency electromagnetic field on a pregnant woman and fetus. The model was exposed to the reference levels of the electric and magnetic field recommended by the ICNIRP guidelines. The induced current densities were calculated by finite element method and results were compared to the basic restriction recommended by the ICNIRP. The basic restrictions were met in all tissues and all orientations of the electric and magnetic fields, except (2.08 mA/m2) when the model was exposed to a vertical electric field and a sagittal magnetic field simultaneously. Since the simultaneous exposure to yields higher induced current values in the body, both should always be taken into account in low-frequency dosimetry numerical modeling. Keywords— Induced electric current, Dosimetry, Pregnancy, Finite element method.

I. INTRODUCTION Induced electric current values have been used as dosimetric measures in quantifying interactions with the electromagnetic fields in the low frequency range. The International Commission on Non-Ionizing Radiation Protection (ICNIRP) set the basic restriction for the general public at 50 Hz to 2 mA/m2 averaged over 1 cm2 perpendicular to the current flow in order to prevent unwanted nerve stimulation of the central nervous system (CNS) [1]. Since the electric current density in the body is very difficult to measure, reference levels for the unperturbed or external electric (5 kV/m) and magnetic (80 A/m) field strength were also set. If the reference levels are met, the basic restrictions for the electric current density should also always be met. The relationship between the reference levels and the basic restrictions was derived for healthy adults and do not necessarily hold for people with medical implants, children or pregnant women and the fetus. Anatomically realistic models have been used to determine the induced low-frequency currents in the human body for the last fifteen years. Several studies used highresolution whole body models to study the interactions between the electric field and the human body [2,3,4] and came to the conclusions that the limits set by ICNIRP were

not exceeded. The same conclusions were reached by Dawson et al. who studied the currents induced by a magnetic field [5] and by Ilvonen et al. who studied currents induced in the brain by the digital mobile phone batteries [6]. The first high-resolution pregnant woman model was developed recently by Cech et al. [7]. The body of the mother was segmented into 37 different tissues and the body of the fetus comprised of the skeleton, soft tissue and the CNS. The calculations demonstrated that basic restrictions were exceeded for the uterus, placenta, bladder and liver and the CNS of the fetus when exposed to the homogeneous vertical electric field. In other fetal tissues and other tissues of the mother the restrictions were met when exposed to the electric field or the magnetic field. All the research so far has concentrated on the exposure to electric or magnetic field, and not on simultaneous exposure to both fields, which is far more common. We developed a simple anatomically realistic model of a pregnant woman comprised of five different tissues and calculated the induced currents due to the exposure to a 50 Hz electromagnetic field. The results were compared to the ICNIRP basic restrictions and to the results of exposure to electric field only. II. METHODS A. Development of pregnant woman model Data for constructing the model were obtained from Shi and Xu [8]. Their CT images of a pregnant woman in the 30th week of gestation covered the portion of the body between the upper thigh and lower breast in 70 slices (without the arms), each 7 mm apart with pixel size of 0.938 mm. The segmentation of the cross-sectional images was performed by a semiautomatic algorithm and 26 different tissues were identified, out of which six tissues were used as the basis for our simplified anatomical model – skin, fat and soft tissue, muscle, uterus, placenta and fetus. The skin was later dropped from the model, after we had established that its presence does not affect the induced current values in the other tissues.

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Numerical Assessment of Induced Current Densities for Pregnant Women Exposed to 50 Hz Electromagnetic Field

Table 1 Conductivities of used tissues assumed for 50 Hz Tissue

Conductivity σ (S/m)

Fat Muscle Fetus Uterus Placenta Other

0.020 0.240 0.310 0.381 0.656 0.216

To build individual 3D tissue models, an algorithm was written in MATLAB R2006a (Mathworks, Massachusetts, US) that used linear interpolation between points on the edges of individual tissue cross-sections. The tissue models were then constructed together in a 3D trunk model. Subsequently the trunk model was inserted into a homogeneous whole-body 3D computer model that was constructed from the cross-sectional images of The Visible Human Project (National Library of Medicine, US) [9]. The size of the homogeneous model was adapted to the pregnant model by linear scaling. The tissue electric properties in Table 1 were taken from Gabriel et al. [11,12], with some exceptions. For the homogeneous parts of the model, a conductivity of 0.216 S/m was chosen, which equals the average properties of human tissue at 50 Hz [7]. For the uterus the values reported by Gabriel (0.230 S/m) were increased to 0.381 S/m to account for the increase in blood flow from the 1st to the 30th week of pregnancy [13]. The assumed conductivity for the fetus was calculated as the average of the fetal conductivity and the conductivity of the amniotic fluid, which was not modeled as a separate tissue due to low resolution of the original images. The placental conductivity was scaled from the uteral conductivity taking into account the larger contents of blood in the placenta [13]. For quasi-static calculations of Maxwell equations the imaginary part of the complex conductivity ωε is small compared to the real part σ, therefore the relative permittivities εr of all tissues were set to 1. B. Numerical method The meshing and the calculations were performed with the COMSOL MultyphysicsTM (COMSOL, Stockholm, Sweden) software package for physical modeling, which has been developed on the basis of the finite element method (FEM). The FEM method calculates partial differential equations (PDEs) on discretized elements of different shapes and sizes, thus enabling very accurate modeling of irregular geometries. Our model of the pregnant women was discretized to 62380 tetrahedral elements of sufficient minimum quality (0.12) to guarantee accurate results [14].

227

The weak form of the quasi-static formulation of Maxwell equations was used to calculate the electric potential and the vector magnetic potential. Calculations were performed with the COMSOL Spooles direct solver. In average, the computing time was 15 minutes and 1.8 GB of memory was used. The exposures of the isolated pregnant woman to the electromagnetic field were simulated for the combination of a homogeneous 5 kV/m vertical electric field and a homogeneous 80A/m magnetic field in the frontal or the sagittal orientation, which is equal to the worst case scenario of reference levels published by ICNIRP [1]. We examined whether the basic restriction – the 1 cm2 average induced current density - were met in the modeled tissues of the mother and fetus. The average current density was determined by averaging the current components normal to the coordinate planes over circular areas of approximately 1 cm2. III. RESULTS The electric current induced in the pregnant woman is largest in the narrow regions of the body, i.e. in the feet, ankles, knees and the neck, as is shown in Figure 1. The largest values of the induced current in the trunk are reached in the placenta and at the boundary between the muscle and fat tissue at the back of the body. When the body was exposed only to the magnetic field, the highest current densities were found in the placenta and the uterus, but not in the legs or the neck. Table 2 shows the induced current densities averaged over 1 cm2 in individual tissues. The highest values of the induced current were calculated in the placenta, where the basic restrictions were not met (2.08 mA/m2), when the pregnant woman was exposed to a vertical electric field and

Table 2 Maximum current densities averaged over 1 cm2 in the fat tissue, muscle, uterus, placenta and fetus induced by frontally Hf or sagittally Hs oriented magnetic field of 80 A/m, vertical electric field E of 5 kV/m, and the combination of the electric and magnetic field. Current density averaged over 1 cm2 (mA/m2) Tissue

Hf

Hs

E

E + Hf

E + Hs

Fat

0.12

0.13

0.13

0.18

0.19

Muscle

0.78

0.87

0.52

1.03

1.14

Fetus

0.71

0.74

0.46

0.76

1.06

Uterus

0.92

1.12

0.62

0.98

1.58

Placenta

1.41

1.42

0.81

1.63

2.08

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Fig. 1 Electric current density distribution in sagittal cross-sections of the pregnant woman at distances x from the assumed central body plane (from left to right x = 12, 8, 4 and 0 cm). All plots are scaled to a maximum range of 2 mA/m2.

a sagittal magnetic field. In all other tissues the highest values resulted from the simultaneous exposure to the electric and magnetic fields in the sagittal orientation as well, while the lowest induced current values were calculated, when the pregnant woman was exposed to vertical electric field only. IV. DISCUSSION Electric current density distributions in a simple, yet anatomically realistic model were calculated for different exposure combinations of electric and magnetic fields. Our results show good agreement with earlier studies, when there is a possibility of comparison. For example, the induced current density values in the uterus for magnetic field exposure in the frontal and sagittal orientation by Cech et al. were determined to be 0.79 mA/m2 and 1.21 mA/m2, respectively [7], while our results are 0.91 mA/m2 and 1.12 mA/m2. A good agreement was also found between maximum current density in the muscle published by Dimbylow (the data was scaled from 1 V/m to 5 kV/m) – 1.31 mA/m2 [3] and the maximum current density obtained in our simulations – 1.23 mA/m2 (data for maximum current densities not shown). The synergistic effects of the electric and magnetic field on the total induced current density are presented in Table 2. It is clear that the induced currents from both fields are not a simple sum of the individual induced currents. In

fact, it seems that the total values are less than 150 % of the sum and in the case of the fetus even less than that (107 %). The reason for this is that the directions of the induced currents are not parallel to each other, which results in a lower total current. The basic restrictions for the pregnant woman and the fetus in our study were met in all tissues, except in the placenta for the case of the combined exposure of a vertical electric field and a sagittal magnetic field. When the electric conductivity of the placenta was lowered by 20 %, the values of the induced current fell below the ICNIRP limits. Accurate measurements of the dielectric properties of the placenta are thus needed to confirm or disprove our choice of conductivity values and whether the basic restrictions were truly exceeded in the placenta. V. CONCLUSIONS Our simulation demonstrated that the basic restrictions were exceeded in the placenta, when the pregnant woman was exposed to a vertical electric field and a sagittal magnetic field simultanouesly. Since simultanoues exposure to electric and magnetic fields causes higher currents to induce in the body, we think that both should always be taken into account in low-frequency dosimetry numerical modeling. Futher investigations into the dielectric properties of biological tissues during pregnancy and more detailed models

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Numerical Assessment of Induced Current Densities for Pregnant Women Exposed to 50 Hz Electromagnetic Field

are required to more accurately evaluate the induced current densities in the pregnant woman and fetus.

ACKNOWLEDGMENT The authors would like to thank Dr Xu of the Rensselaer Polytechnic Institute, Troy, New York for providing us with the original CT images and the segmented images of the pregnant woman. This work was supported by the Slovenian Research Agency.

7. 8.

9. 10.

REFERENCES 1. ICNIRP (1998) Guidelines for limiting exposure to timevarying electric, magnetic and electromagnetic fields (up to 300 GHz). Health Phys 74:494–522 2. Furse CM and Gandhi OP (1998) Calculation of electric fields and currents induced in a millimeter-resolution human model at 60 Hz using the FDTD method. Bioelectromagnetics 19:293–299 DOI 10.1002/(SICI)1521-186X(1998)19:5<293:: AID-BEM3>3.0.CO;2-X 3. Dimbylow PJ (2000) Current densities in a 2 mm resolution anatomically realistic model of the body induced by low frequency electric fields. Phys Med Biol 45:1013–1022 DOI 10.1088/0031-9155/45/4/315 4. Hirata A, Caputa K, Dawson TW et al (2001) Dosimetry in models of child and adult for low-frequency electric field. IEEE Trans Biomed Eng 48:1007–1012 DOI 10.1109/ 10.942590 5. Dawson TM and Stuchly MA (1998) High-resolution organ dosimetry for human exposure to low-frequency magnetic fields. IEEE Trans Magn 34:708–718 DOI 10.1109/ 20.668071 6. Ilvonen S, Sihvonen A, Kärkkäinen K et al (2005) Numerical assessment of induced ELF currents in the human head due to

11.

12.

13. 14.

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the battery current of a digital mobile phone. Bioelectromagnetics 26:648–652 DOI 10.1002/bem.20159 Cech R, Leitgeb N and Pedliaditis M (2007) Fetal exposure to low frequency electric and magnetic fields. Phys Med Biol 52:879–888 DOI 10.1088/0031-9155/52/4/001 Shi C and Xu X (2004) Development of a 30-week-pregnant female topographic model from computed tomography (CT) images for Monte Carlo organ dose calculations. Med.Phys 31:2491–2497 DOI 10.1118/1.1778836 Valic B (2006) Vpliv implantov na porazdelitev elektromagnetnega polja v človeku. Doctoral dissertation, Univerza v LJubljani, Ljubljana Cech R, Leitgeb N and Pedliaditis M (2007) Fetal exposure to low frequency electric and magnetic fields. Phys Med Biol 52:879–888 DOI 10.1088/0031-9155/52/4/001 Gabriel S, Lau RW and Gabriel C (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 41: 2251–2269 DOI 10.1088/0031-9155/41/11/002 Gabriel S, Lau RW and Gabriel C (1996) The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Phys Med Biol 41:2271–2293 DOI 10.1088/0031-9155/41/11/003 International commission on radiological protection (1975) Basic anatomical and physiological data for use in radiological protection: reference values. Pergamon, New York COMSOL at http://www.comsol.com/products/multiphysics/ (Feb 21, 2007) Author: Anze Zupanic Institute: Street: City: Country: Email:

University of Ljubljana, Faculty of Electrical Engineering Trzaska 25 Ljubljana Slovenia [email protected]

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Regenerative Effects of (-)-epigallocatechin-gallate Against Hepatic Oxidative Stress Resulted by Mobile Phone Exposure E. Ozgur1, G. Güler1 and N. Seyhan1 1

Gazi University Faculty of Medicine/Biophysics Department, Ankara, Turkiye

Abstract— Many in vivo and in vitro studies have been performed to investigate the biological consequences and to assess health risks of RFR (Radio Frequency Radiation) generated from mobile phones. Mechanism of RFR and oxidative damage and the question if antioxidants taken as nutrition can alter the oxidative damage of mobile phone damage are popular subjects tried to investigate. In this study, it was aimed to investigate whether the antioxidative effects of green tea catechins can inhibit RFR- induced free radical releases causing oxidative damage of proteins in guinea pigs’ liver tissue. RFR generated by mobile phone with 0.81 W/kg digital SAR value operating in GSM 1800 MHz frequency. Male Guinea pigs were exposed to mobile phone radiation averaged as 11.2 V/m, measured during exposure for 20 minutes in 7 days of a week. Activities of antioxidant enzymes such as superoxide dimutase (SOD), glutathione peroxidase (GSH-Px), and level of malondialdehyde (MDA) were measured in liver guinea pigs which divided into four groups as control, EGCG-treated, mobile phone-exposed and both mobile phone-exposed and EGCG-treated. As a result, both antioxidant enzyme activities and free radical levels of the mobile phone exposed and mobile phone exposed with EGCG groups changed significantly ( p < 0.05). Keywords— mobile phone, Radio Frequency, free radical, EGCG, antioxidant.

I. INTRODUCTION Radio frequency, or RF, refers to that portion of the electromagnetic spectrum in which electromagnetic waves can be generated by alternating current fed to an antenna. Wireless telephones are hand-held phones with built-in antennas, often called cell, mobile, or PCS phones. These phones are popular with callers because they can be carried easily from place to place. Wireless telephones are two-way radios. When you talk into a wireless telephone, it picks up your voice and converts the sound to radiofrequency energy (or radio waves). The radio waves travel through the air until they reach a receiver at a nearby base station. The base station then sends your call through the telephone network until it reaches the person you are calling. When you receive a call on your wireless telephone, the message travels through the telephone network until it reaches a base station close to your wireless phone. Then the base station sends

out radio waves that are detected by a receiver in your telephone, where the signals are changed back into the sound of a voice [1]. Radiofrequency fields of cellular mobile phones may affect biological systems by increasing free radicals, which appear mainly to enhance lipid peroxidation, and by changing the antioxidant defense systems of human tissues, thus leading to oxidative stress which can be depressed by antioxidants such as green tea catechins, (-)epigallocatechin-gallate (EGCG). Epigallocatechin gallate belongs to the family of catechins. It contains 3 phenol rings and has very strong antixoidant properties. EGCG, possess the most potent antioxidant activity of the catechins is the main active component if green tea leaves. Its possible benefit as a nutritional chemopreventive agent for cancer, atherosclerosis, and neurodegenerative diseases is generating increased scientific interest. Epigallocatechin gallate may provide health effects by protecting our cells from oxidative damage from free radicals. A number of chronic disease have been associated with free radical damage, including cancer, arteriosclerosis, heart diseases and accelerated aging. Epigallocatechin gallate can interfere with many enzyme systems [2,3]. In this research, effects of RFR generated by mobile phones on Malondialdehyde (MDA, an index of lipid peroxidation) were used as markers of oxidative stress-induced hepatic impairment. Superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities were studied to evaluate the changes of antioxidant status. II. MATERIAL AND METHOD A. Exposure Details In this investigation, 40 each three-month-old Guinea pigs divided into four groups as control, EGCG-treated, mobile phone-exposed and mobile phone-exposed with the treatment of EGCG. Both mobile phone exposure and EGCG-treated (intraperitoneally) groups were exposed to RFR, radiated from Nokia 3210 mobile phone operated in GSM 1800 MHz. During the exposure of every Guinea pig, external E fields were measured by NARDA EMR 300 and type 8.3 probe. Measurements were taken for duration of 10

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Regenerative Effects of (-)-epigallocatechin-gallate Against Hepatic Oxidative Stress Resulted by Mobile Phone Exposure

minutes per 2 seconds and the data saved to the computer connected to device via fiber optic cable. The whole data were averaged before statistical analysis. Guinea pigs were exposed to RFR averaged as 11.2 V/m for 10 minutes a day during 7 days and analyzed for the effects on liver tissue activities of SOD, GSH-Px and the levels of MDA.

Table 1 Liver oxidant and antioxidant levels in all groups. Results are expressed as mean±standart deviation MDA (nmol/mg.pr)

Controls

B. Liver collection, storage and biochemical process After the last day of mobile phone exposure, liver tissues were removed after decapitation of control and exposed animals. They were immediately frozen in liquid nitrogen and stored at -80ºC until analysis. MDA level were determined in the homogenate [4], GSH-Px activities in the supernatant [5] and SOD activity in extract samples according to the methods described elsewhere [6,7]. Protein measurements were made at all stages according to the method of Lowry et al.

215

EGCGtreated Exposure group Exposure group with EGCG

SOD (U/mg pr)

GSH-Px (U/mg pr)

39,47888 ± 3,296332

31,00308 ± 2,491115

15,350475 ±1,011348066 55,3026 ±8,572794953

29,32763 ± 4,969521 16,54091 ± 3,094963

28,584 ± 4,840640217 28,634 ± 3,122963

49,681525 ±4,800276674

22,40058 ± 3,529115

25,938125 ±3,548873601

21,60233333 ± 7,989324846

C. Other Experimental Features Approximately 3 month old male guinea pigs, weighing 250-300 g, were obtained from Turkey Hifzissiha Institute, Ankara, Turkiye. They were maintained on a 12:12 h light – dark cycle and housed in the suitable cages where temperature 22-24ºC and humidity levels were controlled and were fed ad libitum on a standard lab chow and carrot. Since placing more than one animal in a cage would create a stress factor, only one animal was placed in each cage during each RFR exposure period.

Figure 1 MDA level of liver

under the exposure of RFR and treatment of EGCG

D. Statistical Analysis Data were expressed as mean ± standard deviation. Statistical analyses with Kruscal Wallis were performed on the data of the biochemical variables to examine differences III. RESULT Significant differences were found in mobile phone exposure groups with controls for all parameters. SOD activities decreased statistically with respect to controls, exposure group and exposure group with EGCG. However, decrease of GSH-Px is insignificant. MDA level changed significantly in both mobile phone radiation groups and EGCG- treated groups. The results are presented in Table 1 and Figure 1-3.

Figure 2 SOD activities of liver under the exposure of RFR and treatment of EGCG

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E. Ozgur, G. Güler and N. Seyhan

Figure 3 GSH-Px activities of liver under the exposure of RFR and treatment of EGCG

IV. CONCLUSIONS The radiofrequency (RF) signals emitted from mobile phones are considered as a type of non-ionizing electromagnetic radiation and are perceived as health risk. More focus has been brought on the potential effect of RFR to elicit biological response, in association with its detrimental effects on widespread application and long-term induction. The biological effects as a result of RF exposure include changes in cell membrane function, metabolism, and cellular signal communication, activation of proto-oncogene and cell death. There are scientific evidences on the health hazards due to RF-EMFs, like neurodegenerative disorders, brain tumors and cancer. Most of these disorders are result of increased free radicals which are atoms or groups of atoms with an odd (unpaired) number of electrons and can be formed when oxygen interacts with certain molecules. Once formed these highly reactive radicals can start a chain reaction, like dominoes. Their chief danger comes from the damage they can do when they react with important cellular components such as DNA, or the cell membrane. Cells may function poorly or die if this occurs. To prevent free radical damage the body has a defense system of antioxidants. Antioxidants are molecules which can safely interact with free radicals and terminate the chain reaction before vital molecules are damaged. Although there are several enzyme systems within the body that scavenge free radicals, these may be inadequate for some cases, so Antioxidants must be supplied in the diet. EGCG is one of the powerful antioxidants reducing the level of free radicals causing cell damage [9]. In light of the results of these studies, we performed a study on mobile phone radiation to analyze the effect of not only RF fields but also EGCG on free radical levels and

antioxidant enzyme activities on an in vivo system, guinea pigs. Yurekli et al. studied effects of GSM base transceiver station (BTS) frequency of 945 MHz on oxidative stress in rats and they conclude that MDA and GSH levels and SOD activities changed significantly [10]. Other study owing to Ozguner et al. was to investigate the effect of mobile phone on the level of Malondialdehyde (MDA), and nitric oxide (NO) and activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) activities. Also they ask if a antioxidant (to Caffeic acid phenethyl ester (CAPE)) applied change the effect of mobile phone radiation. So they conclude that tissue MDA and NO levels increased, SOD, CAT and GSHPx activities were reduced in mobile phone exposed group, while CAPE treatment reversed these effects [11]. Moreover, in our study, the increased levels of MDA and NO and the decreased levels of hepatic SOD, GSH-Px and MPO activities demonstrate the role of oxidative mechanisms in mobile phone-induced liver tissue damage, and, EGCG via its free radical scavenging and antioxidant properties, ameliorates oxidative liver injury. These results show that EGCG can exhibit a protective effect on mobile phoneinduced and free radical mediated oxidative liver impairment in guinea pigs.

REFERENCES 1. FDA at http://www.fda.gov/cellphones/ 2. Leung LK, Su Y, Chen R et al.. (2001) Theaflavins in Black Tea and Catechins in Green Tea Are Equally Effective Antioxidants. J Nutr. 131: 2248-51 3. Waltner-Law ME, Wang XL, Law BK et al. (2002) Epigallocatechin Gallate, a Constituent of Green Tea, Represses Hepatic Glucose Production. J Biol Chem.: 277:34933-40 4. Wasowics W, Neve S, Peretz A (1993) Optimized steps in fluorometric determination of thiobarbituric acid reactive substances in serum: importance of extraction pH and influence of sample preservation and storage. Clin Chem,: 39, 2522-2526 5. Paglia DE, Valentine WN (1967) Studies on the quantitative and qualitative characterization of erythrocyte glutathione peroxidase. J Lab Clin Med:70, 158-169 6. Sun Y, Oberly LW, Li,Y (1988) A simple method for clinical assay of superoxide dismutase. Clin. Chem: 34, 497-500 7. Durak I, Yurtarslani Z, Canbolat O et al.(1993) A methodological approach to superoxide dismutase (SOD) activity assay based on inhibition of nitroblue tetrazolium (NBT) reduction. Clin. Chem Acta:214,103-104 8. Lowry OH, Rosenbough NJ, Farr AL et al.(1951) Protein measurement with Folin-phenol reagent. J Biol Chem:193, 265-275

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Regenerative Effects of (-)-epigallocatechin-gallate Against Hepatic Oxidative Stress Resulted by Mobile Phone Exposure 9. Ravindra T, Ahuja YR, Bhargava SC, Lakshmı NK (2006)Epidemiological Study to Assess The Risk of RFEMFs from Mobile Telephony, 4th International Workshop on Biological Effects of Electromagnetic Field, Crete, Greece, 2006, pp 1205-1208 10. Yurekli AI, Ozkan M, Kalkan T et al.(2006) GSM base station electromagnetic radiation and oxidative stress in rats. Electromagn Biol Med:25,177-88

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11. Ozguner F, Altinbas A, Ozaydin M (2005) Mobile phoneinduced myocardial oxidative stress: protection by a novel antioxidant agent caffeic acid phenethyl ester. Toxicol Ind Health: 21,223-30 Author:

Elcin Ozgur

Institute: Gazi University Faculty of Medicine, # Biophysics Department Street: Besevler City: Ankara Country: Turkiye Email: [email protected]

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The Relation Assessment Between 50 Hz Electric Field Exposure-Induced Protein Carbonyl Levels and The Protective Effect of Green Tea Catechin (EGCG) A. Tomruk1, G. Guler1 and N. Seyhan1 1

Gazi University Faculty of Medicine/Biophysics Department, Ankara, Turkiye

Abstract— The aim of this study was to determine the oxidation of proteins, measuring the protein carbonyl levels (PCO) as biomarkers of oxidative stress, and to answer whether protective effect of green tea catechin, EGCG, can reduce the protein damage initiated by free radicals in guinea pigs’ liver tissues under 50 Hz 12 kV/m E Field exposure. Guinea pigs, weighted 250-300g, were used in the study. Protein carbonyl levels (PCO) were measured spectrophotometrically, by Levine method with slight modifications. Mann-Whitney U test was applied for statistical analysis. As a result, both 50 Hz 12 kV/m E Field exposure and EGCG-administrated (intraperitoneally) groups’ liver protein carbonyl levels were found nonsignificantly decreased than control groups. However 50 Hz 12 kV/m E Field exposure + EGCG-administrated group’s liver protein carbonyl levels were found significantly decreased. In the view of these results, it was concluded that E-Field exposure may inhibit the formation oxidized protein, reducing the protective effect of EGCG. Keywords— ELF-E Field, protein carbonyl level (PCO), EGCG, catechin, liver.

I. INTRODUCTION Since Extremely Low Frequency (ELF) Electromagnetic Fields (EMF) are capable of inducing electric current and fields in the tissues of exposed subjects, researches in possible biological effects of these fields have increased [1,2].Some of them demonstrated that ELF Electric and Magnetic Fields can affect the rates of DNA, RNA and protein synthesis[3-8]. Scientists have also suggested that ELF Electric and Magnetic Fields cause an increase in free radical activity in living organisms, leading to formation of excessive amounts of active oxygen forms. Active oxygen forms, attending enzyme activities, gene expressions and affecting membrane structures and functions, can cause vital damages in biomolecules [9,10]. Reactive oxygen species (ROS) may damage all types of biological molecules. Oxidative damages to proteins, lipids or DNA may all be seriously deleterious and may be concomitant. However, proteins are possibly the most immediate vehicle for inflicting oxidative damage on cells because they are often catalysts rather than other mediators; thus the effect of damage to one molecule is greater [11,12].

Protein carbonyl content is the most general indicator and the most commonly used marker of protein oxidation. The use of the protein carbonyl contents as biomarkers of oxidative stress provide some advantages due to their early formation and their relative stability in comparison with the measurement of other oxidation products. While oxidized proteins are degraded by cells within days, lipid peroxidation products are detoxified within minutes [11,13-15]. Antioxidants are chemicals that reduce the rate of oxidation reactions. Oxidation reactions are chemical reactions that involve the transfer of electron from one substance to an oxidizing agent. Antioxidants can slow these reactions either by reacting with intermediates and halting the oxidation reaction directly, or by reacting with the oxidizing agent and preventing the oxidation reaction from occurring. Recent studies have showed that antioxidants play an important role as health promoters in many diseases, such as cancer and aging [16]. Green Tea is one of the most popular beverage because its consumption has been suggested to have many beneficial health effects, including the prevention of cancer and heart diseases [17]. Most of the therapeutic benefits of green tea are due to the catechins, which are polyphenols with a flavanoid structure. Polyphenols are “free radical scavengers” which eliminate hydroxyl radicals, superoxide anion radicals, 1,1-Dipehnyl-2-picrylhydrazyl (DPPH) radicals and other radicals. Four major green tea catechins exist, namely (-)-epicatechin (EC), (-)-epigallocatechin (EGC), (-)epicatechin gallate (ECg), (-)-epigallocatechin gallate (EGCG). The catechins comprise approximately 30% to 42% of the total green tea solids, and a typical cup of green tea contains between 300 and 400 mg polyphenols, of which 10-30 mg is EGCG. EGCG has been shown to be potent antioxidants in many chemicals and biochemical studies [18,19]. The aims of this study were the demonstration of oxidative stress, using protein carbonyl contents as biomarkers and the determination of antioxidative effect of green tea extract, EGCG, in the condition of oxidative stress in guinea pigs’ liver tissues, under 50 Hz, 12 kV/m E Field exposure.

T. Jarm, P. Kramar, A. Županič (Eds.): Medicon 2007, IFMBE Proceedings 16, pp. 230–233, 2007 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007

The Relation Assessment Between Electric Field Exposure-Induced Protein Carbonyl Levels and The Prote