ECG Signal Processing, Classification and Interpretation : A Comprehensive Framework of Computational Intelligence. 1st ed. 2012

種類:

電子ブック

責任表示:

edited by Adam Gacek, Witold Pedrycz

出版情報:

London : Springer London : Imprint: Springer, 2012

著者名:

• Gacek, Adam.
• Pedrycz, Witold.
• SpringerLink (Online service)

ISBN:

9780857298683 [0857298682]    

注記:

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 Comp

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