Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques : A MATLAB Based Approach
Language: English
Published by Elsevier Inc Mär 2019, 2019
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - 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.…
Seller Inventory # 9780128174449
- Title
- Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques : A MATLAB Based Approach
- Author
- Abdulhamit Subasi
- Publisher
- Elsevier Inc Mär 2019
- Publication year
- 2019
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128174447
- ISBN 13
- 9780128174449
- Item weight
- 1,050 grams
- Dimensions
- 279x216x23 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Analysis and Security. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences.
"About the title" may belong to another edition of this title.
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