简介:

Advanced Biosignal Processing By Amine Nait-Ali Publisher: Springer Number Of Pages: 374 Publication Date: 2009-04-23 ISBN-10 / ASIN: 3540895051 ISBN-13 / EAN: 9783540895053 Product Description: Through 17 chapters, this book presents the principle of many advanced biosignal processing techniques. After an important chapter introducing the main biosignal properties as well as the most recent acquisition techniques, it highlights five specific parts which build the body of this book. Each part concerns one of the most intensively used biosignals in the clinical routine, namely the Electrocardiogram (ECG), the Elektroenzephalogram (EEG), the Electromyogram (EMG) and the Evoked Potential (EP). In addition, each part gathers a certain number of chapters related to analysis, detection, classification, source separation and feature extraction. These aspects are explored by means of various advanced signal processing approaches, namely wavelets, Empirical Modal Decomposition, Neural networks, Markov models, Metaheuristics as well as hybrid approaches including wavelet networks, and neuro-fuzzy networks. The last part, concerns the Multimodal Biosignal processing, in which we present two different chapters related to the biomedical compression and the data fusion. Instead organising the chapters by approaches, the present book has been voluntarily structured according to signal categories (ECG, EEG, EMG, EP). This helps the reader, interested in a specific field, to assimilate easily the techniques dedicated to a given class of biosignals. Furthermore, most of signals used for illustration purpose in this book can be downloaded from the Medical Database for the Evaluation of Image and Signal Processing Algorithm. These materials assist considerably the user in evaluating the performances of their developed algorithms. This book is suited for final year graduate students, engineers and researchers in biomedical engineering and practicing engineers in biomedical science and medical physics. Table of contents Introduction.- Biosignal properties and acquisition.- ECG analysis: wavelet based approaches.- ECG analysis: Empirical Mode Decomposition (EMD) based approach.- Extraction of ECG characteristics using source separation techniques.- Statistical models based ECG classification.- ECG compression: a review.- EEG analysis and feature extraction.- Neural networks and hybrid approaches for EEG classification.- Source separation for multichannel EEG.- EMG analysis: time-frequency based techniques.- Empirical Mode Decomposition for EMG feature extraction.- EMG pattern recognition.- EP analysis.- EP classification.- Optimal approaches for EP denoising.- Integration of EEG in functional MRI.- Multimodal compression of biomedical data.- Conclusion.