The book 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. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, 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. Illustrative material includes: brief numerical experiments; detailed schemes, exercises and more advanced problems.

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.