Synopses & Reviews
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.
This concise book presents the principles of many advanced biosignal processing techniques. The book has been voluntarily structured according to signal categories. This helps the reader to assimilate the techniques dedicated to a given class of biosignals.
Table of Contents
Preface: Amine Naït-Ali Chapter 1: Biosignals: acquisition and general properties Amine Naït-Ali and Patrick Karasinski Chapter 2: Extraction of ECG characteristics using source separation techniques Vicente Zarzozo Chapter 3: ECG processing for exercise test Olivier Meste, Hervé Rix and Grégory Blain Chapter 4: Statistical models based ECG classification Rodrigo Varejão Andreão1, Jérôme Boudy, Bernadette Dorizzi, Jean-Marc Boucher and Salim Graja Chapter 5: Newborn Seizure Detection Using both the ECG and EEG via the Heart Rate Variability Time-Frequency Analysis Mostefa Mesbah, Boualem Boashash and Malarvili Balakrishnan Chapter 6: Adaptive tracking of EEG frequency components Laurent Uldry, Cédric Duchêne, Yann Prudat, Micah M. Murray, Jean-Marc Vesin Chapter 7: From EEG signals to brain connectivity: methods and applications in epilepsy Lotfi Senhadji, K. Ansari-Asl and F. Wendling Chapter 8: Neural Network approaches for EEG classification Amitava Chatterje, Amine Naït-Ali and Patrick Siarry Chapter 9: Analysis of event-related potentials using wavelet networks Hartmut Heinrich and Hartmut Dickhaus Chapter 10: Detection of evoked potentials Peter Husar Chapter 11: Visual Evoked Potential Analysis Using Adaptive Chirplet Transform Jie Cui1 and Willy Wong Chapter 12: EMG analysis: time-frequency based techniques Mohamad Khalil, Marwa Chendeb, Mohamad Diab, Catherine Marque and Jacques Duchêne Chapter 13: Pattern Classification Techniques for EMG Decomposition Sarbast Rasheed and Dan Stashuk Chapter 14: Parametrical modeling of some biosignals using optimization metaheuristics Amir Nakib, Amine. Naït-Ali, Virginie Van Wassenhove and Patrick Siarry Chapter 15: Nonlinear analysis of physiological time series Anisoara Ionescu and Kamiar Aminian Chapter 16: Biomedical data processing using HHT: a review Ming-Chya Wu and Norden Huang Chapter 17: Multimodal compression of biomedical data Amine Naït-Ali, Emre Zeybek and Xavier Drouot