Elsevier S & T

Bioelectrical Signal Processing in Cardiac and Neurological Applications

Sörnmo, Leif; Laguna, Pablo

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Table of contents
  • Cover
  • Contentsi
  • Prefacevii
  • Chapter 1. Introduction1
  • 1.1 Biomedical Signal Processing: Objectives and Contexts2
  • 1.2 Basics of Bioelectrical Signals6
  • 1.3 Signal Acquisition and Analysis14
  • 1.4 Performance Evaluation16
  • Bibliography22
  • Chapter 2. The Electroencephalogram„A Brief Background25
  • 2.1 The Nervous System27
  • 2.2 The EEG„Electrical Activity Measured on the Scalp31
  • 2.3 Recording Techniques37
  • 2.4 EEG Applications39
  • Bibliography50
  • Chapter 3. EEG Signal Processing55
  • 3.1 Modeling the EEG Signal55
  • 3.2 Artifacts in the EEG71
  • 3.3 Nonparametric Spectral Analysis91
  • 3.4 Model-based Spectral Analysis103
  • 3.5 EEG Segmentation125
  • 3.6 Joint Time–Frequency Analysis135
  • Bibliography162
  • Problems175
  • Chapter 4. Evoked Potentials181
  • 4.1 Evoked Potential Modalities183
  • 4.2 Noise Characteristics190
  • 4.3 Noise Reduction by Ensemble Averaging192
  • 4.4 Noise Reduction by Linear Filtering241
  • 4.5 Single-Trial Analysis Using Basis Functions253
  • 4.6 Adaptive Analysis Using Basis Functions278
  • 4.7 Wavelets286
  • Bibliography318
  • Problems329
  • Chapter 5. The Electromyogram337
  • 5.1 The Electrical Activity of Muscles338
  • 5.2 Amplitude Estimation in the Surface EMG347
  • 5.3 Spectral Analysis of the Surface EMG361
  • 5.4 Conduction Velocity Estimation365
  • 5.5 Modeling the Intramuscular EMG371
  • 5.6 Intramuscular EMG Signal Decomposition383
  • Bibliography398
  • Problems406
  • Chapter 6. The Electrocardiogram„A Brief Background411
  • 6.1 Electrical Activity of the Heart412
  • 6.2 Generation and Recording of an ECG415
  • 6.3 Heart Rhythms430
  • 6.4 Heartbeat Morphologies438
  • 6.5 Noise and Artifacts440
  • 6.6 Clinical Applications443
  • Bibliography449
  • Chapter 7. ECG Signal Processing453
  • 7.1 Baseline Wander457
  • 7.2 Powerline Interference473
  • 7.3 Muscle Noise Filtering484
  • 7.4 QRS Detection485
  • 7.5 Wave Delineation510
  • 7.6 Data Compression514
  • Bibliography545
  • Problems557
  • Chapter 8. ECG Signal Processing: Heart Rate Variability567
  • 8.1 Acquisition and RR Interval Conditioning568
  • 8.2 Time Domain Measures570
  • 8.3 Heart Rhythm Representations573
  • 8.4 Spectral Analysis of Heart Rate Variability589
  • 8.5 Clustering of Beat Morphologies603
  • 8.6 Dealing with Ectopic Beats605
  • 8.7 Interaction with Other Physiological Signals614
  • Bibliography621
  • Problems628
  • Appendix A. Review of Important Concepts633
  • A.1 Matrix Fundamentals633
  • A.2 Discrete-Time Stochastic Processes642
  • Bibliography648
  • Appendix B. Symbols and Abbreviations649
  • B.1 Mathematical Symbols649
  • B.2 Abbreviations658
  • Index661
Book details
  • Vendor Elsevier S & T
  • SKU 9780124375529R150
  • ISBN-13 9780080527925
  • Author Sörnmo, Leif; Laguna, Pablo
  • Category Science
  • Subject Biotechnology

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Description eBook delivery & access Reviews
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 applications–the 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

· A problem-driven, interdisciplinary presentation of biomedical signal processing
· Focus on methods for processing of bioelectrical signals (ECG, EEG, evoked potentials, EMG)
· Covers both classical and recent signal processing techniques
· Emphasis on model-based statistical signal processing
· Comprehensive exercises and illustrations
· Extensive bibliography
· For companion web site with project descriptions and signals for download see www.biosignal.lth.se