Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more.
This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.
Author(s): Abdulhamit Subasi
Edition: 1
Publisher: Academic Press
Year: 2019
Language: English
Pages: 449
Cover......Page 1
Practical Guide for
Biomedical Signals Analysis
Using Machine Learning
Techniques:
A MATLAB
Based Approach......Page 3
Copyright......Page 4
Dedication......Page 5
Preface......Page 6
Acknowledgments......Page 7
Electroencephalography......Page 8
Electromyography......Page 12
Electrocardiography......Page 17
Phonocardiography......Page 20
Photoplethysmography......Page 23
The Electroretinogram......Page 24
Machine Learning Methods......Page 25
References......Page 26
Electroencephalography......Page 34
EEG Recording Techniques......Page 36
EEG Rhythms and Waveforms......Page 38
Uses of EEG Signals in Epileptic Seizure Detection and Prediction......Page 39
Uses of EEG Signals in Brain-Computer Interfacing......Page 44
Uses of EEG Signals in Migraine Detection......Page 47
Uses of EEG Signals in Source Localization......Page 48
Uses of EEG Signals in Sleep......Page 50
Uses of EEG Signal for Emotion Recognition......Page 53
Introduction......Page 55
EMG Electrodes......Page 56
Signal Digitization......Page 57
The Motor Unit Action Potential......Page 58
Myoelectric Signal Recording......Page 60
Neuromuscular Disorders......Page 61
Uses of EMG Signals in Diagnosis of Neuromuscular Disorders......Page 62
Uses of EMG Signals in Prosthesis Control......Page 63
Uses of EMG Signals in Rehabilitation Robotics......Page 66
Introduction......Page 69
Physiology......Page 70
The ECG Waveform......Page 71
Uses of ECG Signals in Diagnosis of Heart Arrhythmia......Page 72
Uses of ECG Signals in Congestive Heart Failure Detection......Page 76
Uses of ECG Signals in Sleep Apnea Detection......Page 78
Uses of ECG Signals in Fetal Analysis......Page 79
First Heart Sound (S1)......Page 81
Second Heart Sound (S2)......Page 82
Uses of PCG Signals in Diagnosis of Heart Diseases......Page 83
Photoplethysmography......Page 86
Electrogastrogram......Page 88
References......Page 89
Further Reading......Page 94
Continuous-Time Fourier Series Analysis......Page 95
Discrete-Time Fourier Series Analysis......Page 96
Frequency Resolution......Page 102
Periodogram Power Spectral Density......Page 104
Welch Power Spectral Density......Page 110
Parametric Model-Based Methods......Page 111
Autoregressive Model for Spectral Analysis......Page 117
Yule-Walker AR Modeling......Page 118
Covariance Method......Page 125
Modified Covariance Method......Page 132
Burg Method......Page 133
MUSIC Modeling......Page 136
Eigenvector Modeling......Page 138
Time-Frequency Analysis......Page 140
Short-Time Fourier Transform: The Spectrogram......Page 141
Wigner-Ville Distribution......Page 143
Choi-Williams Distribution......Page 146
Wavelet Analysis......Page 148
Continuous Wavelet Transform......Page 149
Discrete Wavelet Transform......Page 151
Stationary Wavelet Transform......Page 154
Wavelet Packet Decomposition......Page 159
Dual Tree Complex Wavelet Transform......Page 162
Tunable Q-Factor Wavelet Transform......Page 168
Flexible Analytic Wavelet Transform......Page 170
Empirical Wavelet Transform......Page 175
Empirical Mode Decomposition......Page 180
Ensemble Empirical Mode Decomposition......Page 186
Complete Ensemble Empirical Mode Decomposition......Page 189
References......Page 196
Introduction......Page 199
Examples for Feature Extraction......Page 200
Statistical Features......Page 205
Examples With Statistical Features......Page 206
Approximate and Sample Entropy......Page 266
Detrended Fluctuation Analysis......Page 267
Principal Component Analysis......Page 273
Independent Component Analysis......Page 275
Linear Discriminant Analysis......Page 276
Electrocardiogram Signal Preprocessing......Page 277
QRS Detection Algorithms......Page 278
References......Page 281
Performance Evaluation Metrics......Page 282
Linear Discriminant Analysis......Page 286
Naïve Bayes......Page 305
k-Nearest Neighbor......Page 319
Artificial Neural Networks......Page 331
Support Vector Machines......Page 379
Decision Tree (DT)......Page 395
Deep Learning......Page 415
References......Page 439
C......Page 440
E......Page 441
F......Page 443
M......Page 444
O......Page 445
S......Page 446
W......Page 447
Y......Page 448
Back Cover......Page 449
