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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
1.6 Other Biomedical Signals
1.7 Machine Learning Methods
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)
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
4. DIMENSION REDUCTION
4.2 Dimension Reduction Algorithms
4.4 Principle Component Analysis
4.6 Independent Component Analysis
4.7 Other techniques
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
- No. of pages:
- © Academic Press 2019
- 19th March 2019
- Academic Press
- Paperback ISBN:
- eBook ISBN:
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 seven book chapters and more than 150 published journal and conference papers. He is also author of the book, “Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques”. He worked at many institutions as an academician and Georgia Institute of Technology, Georgia, USA, as a researcher. He has been awarded with the Queen Effat Award for Excellence in Research, May 2018. Since 2015, he has been working as a Professor of Information Systems at Effat University, Jeddah, Saudi Arabia. He has worked on several projects related to biomedical signal processing and data analysis.
Professor of Information Systems at Effat University, Jeddah, Saudi Arabia
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