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 ones for electrocardiogram, electroencephalogram and electromyogram, are described in a practical and comprehensive way, in order to help the reader with little knowledge on the matter fully comprehend it. The book is split into five chapters, all of them using MATLAB approach. Chapter 1 is an overview of biomedical signals and machine learning techniques. Chapter 2 focuses on biomedical signals such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG). Chapter 3 presents different signal-processing techniques commonly used in the analysis of biomedical signals such as FFT, AR, MUSIC and different time frequency analysis methods including wavelet transform and empirical mode decomposition. Chapter 4 presents some signal de-noising, feature extraction and dimension reduction techniques such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures. Chapter 5 provides an overview of machine learning techniques such as k-NN, ANN, SVM and decision tree classifiers for the detection and classification of biomedical signals.

This book is a valuable source for bioinformaticians, medical doctors, and several members of biomedical field who need to be up to speed on the most recent and promising machine learning techniques for biomedical signals analysis.