ECG Signal Processing, Classification and Interpretation A Comprehen
Electrocardiogram (ECG) signals are among the most important sources of diagnostic information in healthcare so improvements in their analysis may also have telling consequences. Both the underlying signal technology and a burgeoning variety of algorithms
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Adam Gacek • Witold Pedrycz Editors
ECG Signal Processing, Classification and Interpretation A Comprehensive Framework of Computational Intelligence
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Editors Adam Gacek Institute of Medical Technology and Equipment Roosevelta 118 41-800 Zabrze Poland [email protected]
Witold Pedrycz Department of Electrical and Computer Engineering University of Alberta 116 Street 9107 T6G 2V4 Edmonton Alberta Canada [email protected]
ISBN 978-0-85729-867-6 e-ISBN 978-0-85729-868-3 DOI 10.1007/978-0-85729-868-3 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2011938277 © Springer-Verlag London Limited 2012 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of 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 laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
ECG signals are one among the most important sources of diagnostic information. There has been a great deal of progress in the area of signal processing, classification, and interpretation in terms of the underlying signal acquisition technology itself as well as a variety of algorithmic and system developments supported by advanced information technologies. In the past decades, Computational Intelligence (CI) has emerged as a highly synergistic, computationally appealing, and conceptually unified framework supporting intelligent system design and analysis. Computational Intelligence promotes synergy. The key contributing technologies of CI – neurocomputing, fuzzy sets (or information granules and Granular Computing, in general), as well as evolutionary and population-based optimization – exhibit well-focused yet highly complementary research agenda. Neural networks are about learning and constructing nonlinear mappings. Fuzzy sets are concerned with the representation and organization of domain knowledge in terms of information granules – semantically meaningful entities, which are perceived and processed when describing and classifying real-world phenomena. Evolutionary computin
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