Biomedical Signal Analysis E-Book

Rangaraj M. Rangayyan

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: IEEE Press Series on Biomedical Engineering
• Sprache: Englisch
• Veröffentlichungsjahr: 2015

Seitenzahl: 1208

Beschreibung

The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. * Wide range of filtering techniques presented to address various applications * 800 mathematical expressions and equations * Practical questions, problems and laboratory exercises * Includes fractals and chaos theory with biomedical applications

Leseprobe

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MengChu Zhou

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George Zobrist

Kenneth Moore, Director of IEEE Book and Information Services (BIS)

BIOMEDICAL SIGNAL ANALYSIS

Second Edition

RANGARAJ M. RANGAYYAN

Copyright © 2015 by The Institute of Electrical and Electronics Engineers, Inc.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. All rights reserved. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication is available.

ISBN 978-0-470-91139-6

DEDICATION

Mátr dévô bhava Pitr dévô bhava Áchárya dévô bhava

Look upon your mother as your God Look upon your father as your God Look upon your teacher as your God

— from the sacred Vedic hymns of the Taittireeya Upanishad of India.

This book is dedicated to the fond memory of my mother Srimati Padma Srinivasan Rangayyan and my father Sri Srinivasan Mandayam Rangayyan, and to all of my teachers, in particular, Professor Ivaturi Surya Narayana Murthy.

CONTENTS

PREFACE

New Material in the Second Edition

Intended Audience

Teaching and Learning Plan

ACKNOWLEDGMENTS

PREFACE: FIRST EDITION

Background and Motivation

The Problem solving Approach

Intended Readership

Teaching and Learning Plan

ACKNOWLEDGMENTS: FIRST EDITION

ABOUT THE AUTHOR

SYMBOLS AND ABBREVIATIONS

CHAPTER 1 INTRODUCTION TO BIOMEDICAL SIGNALS

1.1 The Nature of Biomedical Signals

1.2 Examples of Biomedical Signals

1.3 Objectives of Biomedical Signal Analysis

1.4 Difficulties in Biomedical Signal Acquisition and Analysis

1.5 Why Use CAD?

1.6 Remarks

1.7 Study Questions and Problems

1.8 Laboratory Exercises and Projects

CHAPTER 2 ANALYSIS OF CONCURRENT, COUPLED, AND CORRELATED PROCESSES

2.1 Problem Statement

2.2 Illustration of the Problem with Case Studies

2.3 Application: Segmentation of the PCG into Systolic and Diastolic Parts

2.4 Application: Diagnosis and Monitoring of Sleep Apnea

2.5 Remarks

2.6 Study Questions and Problems

2.7 Laboratory Exercises and Projects

CHAPTER 3 FILTERING FOR REMOVAL OF ARTIFACTS

3.1 Problem Statement

3.2 Random, Structured, and Physiological Noise

3.3 Illustration of the Problem with Case Studies

3.4 Fundamental Concepts of Filtering

3.5 Time domain Filters

3.6 Frequency domain Filters

3.7 Order statistic filters

3.8 Optimal Filtering: The Wiener Filter

3.9 Adaptive Filters for Removal of Interference

3.10 Selecting an Appropriate Filter

3.11 Application: Removal of Artifacts in ERP Signals

3.12 Application: Removal of Artifacts in the ECG

3.13 Application: Adaptive Cancellation of the Maternal ECG to Obtain the Fetal ECG

3.14 Application: Adaptive Cancellation of Muscle contraction Inter ference in VAG Signals

3.15 Remarks

3.16 Study Questions and Problems

3.17 Laboratory Exercises and Projects

CHAPTER 4 DETECTION OF EVENTS

4.1 Problem Statement

4.2 Illustration of the Problem with Case Studies

4.3 Detection of Events and Waves

4.4 Correlation Analysis of EEG Rhythms

4.5 Cross spectral Techniques

4.6 The Matched Filter

4.7 Detection of the P Wave in the ECG

4.8 Homomorphic Filtering and the Complex Cepstrum

4.9 Application: ECG Rhythm Analysis

4.10 Application: Identification of Heart Sounds

4.11 Application: Detection of the Aortic Component of the Second Heart Sound

4.12 Remarks

4.13 Study Questions and Problems

4.14 Laboratory Exercises and Projects

CHAPTER 5 ANALYSIS OF WAVESHAPE AND WAVEFORM COMPLEXITY

5.1 Problem Statement

5.2 Illustration of the Problem with Case Studies

5.3 Analysis of ERPs

5.4 Morphological Analysis of ECG Waves

5.5 Envelope Extraction and Analysis

5.6 Analysis of Activity

5.7 Application: Parameterization of Normal and Ectopic ECG Beats

5.8 Application: Analysis of Exercise ECG

5.9 Application: Quantitative Analysis of the EMG in Relation to Force Exerted

5.10 Application: Analysis of Respiration

5.11 Application: Electrical and Mechanical Correlates of Muscular Contraction

5.12 Application: Statistical Analysis of VAG Signals

5.13 Application: Fractal Analysis of the EMG in Relation to Force

5.14 Remarks

5.15 Study Questions and Problems

5.16 Laboratory Exercises and Projects

CHAPTER 6 FREQUENCY DOMAIN CHARACTERIZATION OF SIGNALS AND SYSTEMS

6.1 Problem Statement

6.2 Illustration of the Problem with Case Studies

6.3 Estimation of the PSD

6.4 Measures Derived from Power Spectral Density Functions

6.5 Application: Evaluation of Prosthetic Heart Valves

6.6 Application: Fractal Analysis of VAG Signals

6.7 Application: Spectral Analysis of EEG Signals

6.8 Remarks

6.9 Study Questions and Problems

6.10 Laboratory Exercises and Projects

CHAPTER 7 MODELING BIOMEDICAL SIGNAL GENERATING PROCESSES AND SYSTEMS

7.1 Problem Statement

7.2 Illustration of the Problem with Case Studies

7.3 Point Processes

7.4 Parametric System Modeling

7.5 Autoregressive or All pole Modeling

7.6 Pole zero Modeling

7.7 Electromechanical Models of Signal Generation

7.8 Application: Analysis of Heart rate Variability

7.9 Application: Spectral Modeling and Analysis of PCG Signals

7.10 Application: Detection of Coronary Artery Disease

7.11 Remarks

7.12 Study Questions and Problems

7.13 Laboratory Exercises and Projects

CHAPTER 8 ANALYSIS OF NONSTATIONARY AND MULTICOMPONENT SIGNALS

8.1 Problem Statement

8.2 Illustration of the Problem with Case Studies

8.3 Time variant Systems

8.4 Fixed Segmentation

8.5 Adaptive Segmentation

8.6 Use of Adaptive Filters for Segmentation

8.7 Wavelets and Time frequency Analysis

8.8 Separation of Mixtures of Signals

8.9 Application: Adaptive Segmentation of EEG Signals

8.10 Application: Adaptive Segmentation of PCG Signals

8.11 Application: Time varying Analysis of HRV

8.12 Application: Detection of Epileptic Seizures in EEG Signals

8.13 Application: Analysis of Crying Sounds of Infants

8.14 Application: Adaptive Time frequency Analysis of VAG Signals

8.15 Remarks

8.16 Study Questions and Problems

8.17 Laboratory Exercises and Projects

CHAPTER 9 PATTERN CLASSIFICATION AND DIAGNOSTIC DECISION

9.1 Problem Statement

9.2 Illustration of the Problem with Case Studies

9.3 Pattern Classification

9.4 Supervised Pattern Classification

9.5 Unsupervised Pattern Classification

9.6 Probabilistic Models and Statistical Decision

9.7 Logistic Regression Analysis

9.8 Neural Networks

9.9 Measures of Diagnostic Accuracy and Cost

9.10 Reliability of Features, Classifiers, and Decisions

9.11 Application: Normal versus Ectopic ECG Beats

9.12 Application: Detection of Knee joint Cartilage Pathology

9.13 Remarks

9.14 Study Questions and Problems

9.15 Laboratory Exercises and Projects

REFERENCES

INDEX

EULA

List of Tables

Chapter 9

Table 9.1

Table 9.2

Table 9.3

Table 9.4

Table 9.5

Table 9.6

Table 9.7