Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach

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 signals analysis. Different machine learning applications in biomedical signals analysis, including those for electrocardiogram, electroencephalogram, and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover 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 can serve as a resource 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.

Features

• Provides comprehensive knowledge in the application of machine learning tools in biomedical signals 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
• Includes examples using MATLAB throughout the book, with some examples using Signal Processing Toolbox, Statistics and Machine Learning Toolbox, and Deep Learning Toolbox