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ECG Signal Processing, Classification and Interpretation

by Gacek, Adam.
Authors: Pedrycz, Witold.%editor. | SpringerLink (Online service) Physical details: X, 278p. 104 illus., 61 illus. in color. online resource. ISBN: 0857298682 Subject(s): Engineering. | Cardiology. | Biomedical engineering. | Engineering. | Biomedical Engineering. | Signal, Image and Speech Processing. | Cardiology. | Computational Intelligence.
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Part I: Introduction -- Introduction to ECG Signal Processing -- Fuzzy Sets: A Primer -- Neural Networks and Neurocomputing -- Evolutionary and Population-based Optimization -- Part II: Techniques and Models of Computational Intelligence for ECG Signal Analysis and Classification -- Neurocomputing in ECG Signal Classification -- Knowledge-based Representation and Processing of ECG Signals: A Fuzzy Set Approach -- Evolutionary Optimization of ECG Signal Analysis and Classification -- Granular Models of ECG Signal Analysis and Their Refinements and Abstractions -- Hybrid Architectures of ECG Analyzers and Classifiers. Part III: Computational-intelligence-based ECG System Diagnostic, Interpretation and Knowledge Acquisition Architectures -- Diagnostic ECG Systems and Computational Intelligence: Development Issues -- Interpretation of ECG Signals: A Systems Approach -- Knowledge Representation and ECG Diagnostic and Interpretation Systems.

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 and systems developments have proved successful targets for recent rapid advances in research. ECG Signal Processing, Classification and Interpretation shows how the various paradigms of Computational Intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. Neural networks do well at capturing the nonlinear nature of the signals, information granules realized as fuzzy sets help to confer interpretability on the data and evolutionary optimization may be critical in supporting the structural development of ECG classifiers and models of ECG signals. The contributors address concepts, methodology, algorithms, and case studies and applications exploiting the paradigm of Computational Intelligence as a conceptually appealing and practically sound technology for ECG signal processing. The text is self-contained, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: ·         Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; ·         Part II deals with techniques and models of computational intelligence that are suitable for  signal processing; and ·         Part III details ECG system-diagnostic interpretation and knowledge acquisition architectures. A wealth of carefully organized illustrative material is included: brief numerical experiments; detailed schemes, and more advanced problems. ECG Signal Processing, Classification and Interpretation will appeal to engineers working in the field of medical equipment and to researchers investigating biomedical signal processing, bioinformatics, Computational Intelligence and its applications, bioengineering and instrumentation. The three-part structure of the material also makes the book a useful reference source for graduate students in these disciplines.

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