Biomedical Signal Analysis

BIOMEDICAL SIGNAL ANALYSIS
 Dedication
 Contents
 Preface
 Acknowledgments
 Preface: First Edition
 Acknowledgments: First Edition
 About the Author
 Symbols and Abbreviations
 1 Introduction to Biomedical Signals
 1.1 The Nature of Biomedical Signals
 1.2 Examples of Biomedical Signals
 1.2.1 The action potential of a cardiac myocyte
 1.2.2 The action potential of a neuron
 1.2.3 The electroneurogram (ENG)
 1.2.4 The electromyogram (EMG)
 1.2.5 The electrocardiogram (ECG)
 1.2.6 The electroencephalogram (EEG)
 1.2.7 Eventrelated potentials (ERPs)
 1.2.8 The electrogastrogram (EGG) 1.2.9 The phonocardiogram (PCG)1.2.10 The carotid pulse
 1.2.11 Signals from catheter tip sensors
 1.2.12 The speech signal
 1.2.13 The vibromyogram (VMG)
 1.2.14 The vibroarthrogram (VAG)
 1.2.15 Otoacoustic emission (OAE) signals
 1.2.16 Bioacoustic signals
 1.3 Objectives of Biomedical Signal Analysis
 1.4 Difficulties in Biomedical Signal Analysis
 1.5 Why Use CAD?
 1.6 Remarks
 1.7 Study Questions and Problems
 1.8 Laboratory Exercises and Projects
 2 Concurrent, Coupled, and Correlated Processes
 2.1 Problem Statement
 2.2 Illustration of the Problem with Case Studies 2.2.1 The ECG and the PCG2.2.2 The PCG and the carotid pulse
 2.2.3 The ECG and the atrial electrogram
 2.2.4 Cardiorespiratory interaction
 2.2.5 The importance of HRV
 2.2.6 The EMG and VMG
 2.2.7 The knee joint and muscle vibration signals
 2.3 Application: Segmentation of the PCG
 2.4 Application: Diagnosis and Monitoring of Sleep Apnea
 2.4.1 Monitoring of sleep apnea by polysomnography
 2.4.2 Home monitoring of sleep apnea
 2.4.3 Multivariate and multiorgan analysis
 2.5 Remarks
 2.6 Study Questions and Problems
 2.7 Laboratory Exercises and Projects 3 Filtering for Removal of Artifacts3.1 Problem Statement
 3.2 Random, Structured, and Physiological Noise
 3.2.1 Random noise
 3.2.2 Structured noise
 3.2.3 Physiological interference
 3.2.4 Stationary, nonstationary, and cyclostationary processes
 3.3 Illustration of the Problem with Case Studies
 3.3.1 Noise in event related potentials
 3.3.2 High frequency noise in the ECG
 3.3.3 Motion artifact in the ECG
 3.3.4 Powerline interference in ECG signals
 3.3.5 Maternal interference in fetal ECG
 3.3.6 Muscle contraction interference in VAG signals
 3.3.7 Potential solutions to the problem 3.4 Fundamental Concepts of Filtering3.4.1 Linear shift invariant filters
 3.4.2 Transform domain analysis of signals and systems
 3.4.3 The pole-zero plot
 3.4.4 The discrete Fourier transform
 3.4.5 Properties of the Fourier transform
 3.5 Time domain Filters
 3.5.1 Synchronized averaging
 3.5.2 MA filters
 3.5.3 Derivative based operators to remove low frequency artifacts
 3.5.4 Various specifications of a filter
 3.6 Frequency domain Filters
 3.6.1 Removal of high frequency noise: Butterworth lowpass filters
 3.6.2 Removal of low frequency noise: Butterworth highpass filters