Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
A MATLAB Based Approach
- 1 Edición - 16 de marzo de 2019
- Última edición
- Autor: Abdulhamit Subasi
- Idioma: Inglés
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods c… Leer más
Descripción
Descripción
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.
Puntos claves
Puntos claves
- 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
De interès para
De interès para
Índice
Índice
1. INTRODUCTION and BACKGROUND 1.1 Electroencephalography1.2 Electromyography 1.3 Electrocardiography 1.4 Phonocardiography1.5 Photoplethysmography1.6 Other Biomedical Signals1.7 Machine Learning Methods 1.8 References
2. BIOMEDICAL SIGNALS2.1. 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 Analysis4.7 Other techniques4.8 References
5. CLASSIFICATION METHODS5.1 Linear Regression 5.2 K-Nearest Neighborhood 5.3 Artificial Neural Networks5.4 Support Vector Machines 5.5 Decision Tree Classifiers5.6 Deep Learning5.7 References
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 16 de marzo de 2019
- Idioma: Inglés
Sobre el autor
Sobre el autor
AS
Abdulhamit Subasi
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.