ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence

✍ Scribed by Jarosław Wasilewski, Lech Poloński (auth.), Adam Gacek, Witold Pedrycz (eds.)

• Publisher: Springer-Verlag London
• Year: 2012
• Tongue: English
• Leaves: 289
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
An Introduction to ECG Interpretation....Pages 1-20
An Introduction to ECG Signal Processing and Analysis....Pages 21-46
ECG Signal Analysis, Classification, and Interpretation: A Framework of Computational Intelligence....Pages 47-77
A Generic and Patient-Specific Electrocardiogram Signal Classification System....Pages 79-98
Model-Based Atrial Fibrillation Detection....Pages 99-133
An Introduction to the Use of Evolutionary Computation Techniques for Dealing with ECG Signals....Pages 135-153
Electrocardiogram Application Based on Heart Rate Variability Ontology and Fuzzy Markup Language....Pages 155-178
Learning in Artificial Neural Networks....Pages 179-193
A Review of Kernel Methods in ECG Signal Classification....Pages 195-217
Hyperellipsoidal Classifier for Beat Classification in ECG Signals....Pages 219-236
Evolutionary Optimization of ECG Feature Extraction Methods: Applications to the Monitoring of Adult Myocardial Ischemia and Neonatal Apnea Bradycardia Events....Pages 237-273
Back Matter....Pages 275-278

✦ Subjects

Biomedical Engineering; Signal, Image and Speech Processing; Cardiology; Computational Intelligence