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ECG Signal Processing, Classification and Interpretation A Comprehensive Framework of Computational Intelligence /
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 success...
| Cote: | Libro Electrónico |
|---|---|
| Collectivité auteur: | |
| Autres auteurs: | , |
| Format: | Électronique eBook |
| Langue: | Inglés |
| Publié: |
London :
Springer London : Imprint: Springer,
2012.
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| Édition: | 1st ed. 2012. |
| Sujets: | |
| Accès en ligne: | Texto Completo |
Table des matières:
- 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.