Advanced biosignal processing

著者

    • Naït-Ali, Amine

書誌事項

Advanced biosignal processing

Amine Naït-Ali (ed.)

Springer, c2009

  • : hbk

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

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 toc- 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.

目次

Preface: Amine Nait-Ali Chapter 1: Biosignals: acquisition and general properties Amine Nait-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, Herve Rix and Gregory Blain Chapter 4: Statistical models based ECG classification Rodrigo Varejao Andreao1, Jerome 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, Cedric Duchene, 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 Nait-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 Duchene 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. Nait-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 Nait-Ali, Emre Zeybek and Xavier Drouot

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