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.

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.