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 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 Biography: Prof. Dr. Abdulhamit Subasi is specialized in Machine Learning, Data mining and Biomedical Signal Processing. Concerning application of machine learning to different fields, he wrote five book chapters and he has more than 130 papers in his field. He has been awarded with Queen Effat Award for Excellence in Research, May 2018. Since 2015, he is working as a Professor of Information Systems at Effat University, Jeddah, Saudi Arabia. He is working as director of Research and Consultancy Institute of Effat University. He has worked on several projects related to biomedical signal processing and pattern classification.

1. Introduction and Background 2. Biomedical Signals 3. Biomedical Signal Processing Techniques 4. Dimension Reduction 5. Classification Methods