Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

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.

• Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction
• Explains how to apply machine learning techniques to EEG, ECG and EMG signals
• Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series

Bioinformaticians; clinicians; medical doctors; neuroscientists; cardiologists

1. INTRODUCTION and BACKGROUND 
1.1 Electroencephalography
1.2 Electromyography 
1.3 Electrocardiography 
1.4 Phonocardiography
1.5 Photoplethysmography
1.6 Other Biomedical Signals
1.7 Machine Learning Methods 
1.8 References 

2. BIOMEDICAL SIGNALS
2.1. The Electroencephalogram (EEG) 
2.2. The Electromyogram (EMG)
2.3. The Electrocardiogram (ECG) 
2.4. The Phonocardiogram (PCG)
2.5. The Photoplethysmogram (PPG)
2.7. References 

3. BIOMEDICAL SIGNAL PROCESSING TECHNIQUES 
3.1 Introduction to Spectral Analysis 
3.2 The Fourier Transform 
3.3 Parametric model-based methods 
3.4 Eigen Analysis Frequency Estimation 
3.5 Time–Frequency Analysis Methods 
3.6 References 

4. DIMENSION REDUCTION 
4.1 Introduction 
4.2 Dimension Reduction Algorithms 
4.4 Principle Component Analysis
4.6 Independent Component Analysis
4.7  Other techniques
4.8  References

5. CLASSIFICATION METHODS
5.1 Linear Regression 
5.2 K-Nearest Neighborhood 
5.3 Artificial Neural Networks
5.4 Support Vector Machines 
5.5 Decision Tree Classifiers
5.6 Deep Learning
5.7 References