Bioelectrical Signal Processing in Cardiac and Neurological ApplicationsBy
- Leif Sörnmo, Department of Electrical Engineering, Lund University Lund, Sweden
- Pablo Laguna, Department of Electrical Engineering and Aragon Institute of Engineering Research, Zaragoza University, Zaragoza, Spain.
The analysis of bioelectrical signals continues to receive wide attention in research as well as commercially because novel signal processing techniques have helped to uncover valuable information for improved diagnosis and therapy. This book takes a unique problem-driven approach to biomedical signal processing by considering a wide range of problems in cardiac and neurological applicationsthe two "heavyweight" areas of biomedical signal processing. The interdisciplinary nature of the topic is reflected in how the text interweaves physiological issues with related methodological considerations. Bioelectrical Signal Processing is suitable for a final year undergraduate or graduate course as well as for use as an authoritative reference for practicing engineers, physicians, and researchers. Solutions Manual available online at http://www.textbooks.elsevier.com
Biomedical Engineers, Electrical Engineers, Signal Processing Engineers, Electrocardiologists and other medical professionals involved in biomedical engineering research and biomedical instrument/device development.
Hardbound, 688 Pages
Published: June 2005
Imprint: Academic Press
- Preface1. Introduction1.1 Biomedical Signal Processing: Objectives and Contexts1.2 Basics of Bioelectrical Signals1.3 Signal Acquisition and Analysis1.4 Performance Evaluation2. The Electroencephalogram A Brief Background2.1 The Nervous System2.2 The EEG Electrical Activity Measured on the Scalp2.3 Recording Techniques2.4 EEG Applications3. EEG Signal Processing3.1 Modeling the EEG Signal3.2 Artifacts in the EEG3.3 Nonparametric Spectral Analysis3.4 Model-based Spectral Analysis3.5 EEG Segmentation3.6 Joint Time-Frequency Analysis4. Evoked Potentials4.1 Evoked Potential Modalities4.2 Noise Characteristics4.3 Noise Reduction by Ensemble Averaging4.4 Noise Reduction by Linear Filtering4.5 Single-Trial Analysis Using Basic Functions4.6 Adaptive Analysis Using Basis Functions4.7 Wavelets5. The Electromyogram5.1 The Electrical Activity of Muscles5.2 Amplitude Estimation in the Surface EMG5.3 Spectral Analysis of the Surface EMG5.4 Conduction Velocity Estimation5.5 Modeling the Intramuscular EMG5.6 Intramuscular EMG Signal Decomposition6. The Electrocardiogram A Brief Background6.1 Electrical Activity of the Heart6.2 Generation and Recording of an ECG6.3 Heart Rhythms6.4 Heartbeat Morphologies6.5 Noise and Artifacts6.6 Clinical Applications7. ECG Signal Processing 7.1 Baseline Wander7.2 Powerline Interference (50/60 Hz)7.3 Muscle Noise Filtering7.4 QRS Detection7.5 Wave Delineation7.6 Data Compression8. ECG Signal Processing: Heart Rate Variability8.1 Acquisition and RR Interval Conditioing8.2 Time Domain Measures8.3 Heart Rhythm Representations8.4 Spectral Analysis of Heart Rate Variability8.5 Clustering of Beat Morphologies8.6 Dealing with Ectopic Beats8.7 Interaction with Other Physiological SignalsAppendix A: Review of Important Concepts A.1 Matrix Fundamentals A.2 Discrete-Time Stochastic ProcessAppendix B: Symbols and Abbreviations B.1 Mathematical Symbols B.2 AbbreviationsIndex