COVID-19 Update: We are currently shipping orders daily. However, due to transit disruptions in some geographies, deliveries may be delayed. To provide all customers with timely access to content, we are offering 50% off Science and Technology Print & eBook bundle options. Terms & conditions.
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques - 1st Edition - ISBN: 9780128174449, 9780128176733

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

1st Edition

A MATLAB Based Approach

0.0 star rating Write a review
Author: Abdulhamit Subasi
Paperback ISBN: 9780128174449
eBook ISBN: 9780128176733
Imprint: Academic Press
Published Date: 19th March 2019
Page Count: 456
Sales tax will be calculated at check-out Price includes VAT/GST
209.94
150.00
112.50
115.00
131.00
Unavailable
Price includes VAT/GST

Institutional Subscription

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.

Description

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.

Key Features

  • 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

Readership

Bioinformaticians; clinicians; medical doctors; neuroscientists; cardiologists

Table of Contents

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

Details

No. of pages:
456
Language:
English
Copyright:
© Academic Press 2019
Published:
19th March 2019
Imprint:
Academic Press
Paperback ISBN:
9780128174449
eBook ISBN:
9780128176733

About the Author

Abdulhamit Subasi

Abdulhamit Subasi

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.

Affiliations and Expertise

Professor of Information Systems at Effat University, Jeddah, Saudi Arabia

Ratings and Reviews