Advanced Biosignal Processing

Advanced Biosignal Processing

Advanced Biosignal Processing

Advanced Biosignal Processing

Paperback(Softcover reprint of hardcover 1st ed. 2009)

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Overview

Generally speaking, Biosignals refer to signals recorded from the human body. They can be either electrical (e. g. Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), etc. ) or non-electrical (e. g. breathing, movements, etc. ). The acquisition and processing of such signals play an important role in clinical routines. They are usually considered as major indicators which provide clinicians and physicians with useful information during diagnostic and monitoring processes. In some applications, the purpose is not necessarily medical. It may also be industrial. For instance, a real-time EEG system analysis can be used to control and analyze the vigilance of a car driver. In this case, the purpose of such a system basically consists of preventing crash risks. Furthermore, in certain other appli- tions,asetof biosignals (e. g. ECG,respiratorysignal,EEG,etc. ) can be used - trol or analyze human emotions. This is the case of the famous polygraph system, also known as the “lie detector”, the ef ciency of which remains open to debate! Thus when one is dealing with biosignals, special attention must be given to their acquisition, their analysis and their processing capabilities which constitute the nal stage preceding the clinical diagnosis. Naturally, the diagnosis is based on the information provided by the processing system.

Product Details

ISBN-13: 9783642100451
Publisher: Springer Berlin Heidelberg
Publication date: 12/16/2010
Edition description: Softcover reprint of hardcover 1st ed. 2009
Pages: 378
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

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
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