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Advanced Biosignal Processing

  • Book
  • © 2009

Overview

Editors:
  1. Amine Naït-Ali

    1. Labo. Images, Signaux et, Systèmes Intelligents (LISSI) EA 3956, Université Paris 12, Créteil, France

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  • Concise overview of most all standard method
  • Presents latest developments in Biosignal processing

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Biosignals: Acquisition and General Properties

    • Amine Naït-Ali, Patrick Karasinski
    Pages 1-13

  3. Extraction of ECG Characteristics Using Source Separation Techniques: Exploiting Statistical Independence and Beyond

    • Vicente Zarzoso
    Pages 15-47

  4. ECG Processing for Exercise Test

    • Olivier Meste, Hervé Rix, Grégory Blain
    Pages 49-69

  5. Statistical Models Based ECG Classification

    • Rodrigo Varejão Andreão, Jérôme Boudy, Bernadette Dorizzi, Jean-Marc Boucher, Salim Graja
    Pages 71-93

  6. Heart Rate Variability Time-Frequency Analysis for Newborn Seizure Detection

    • Mostefa Mesbah, Boualem Boashash, Malarvili Balakrishnan, Paul B. Coldiz
    Pages 95-121

  7. Adaptive Tracking of EEG Frequency Components

    • Laurent Uldry, Cédric Duchêne, Yann Prudat, Micah M. Murray, Jean-Marc Vesin
    Pages 123-144

  8. From EEG Signals to Brain Connectivity: Methods and Applications in Epilepsy

    • Lotfi Senhadji, Karim Ansari-Asl, Fabrice Wendling
    Pages 145-164

  9. Neural Network Approaches for EEG Classification

    • Amitava Chatterjee, Amine Naït-Ali, Patrick Siarry
    Pages 165-182

  10. Analysis of Event-Related Potentials Using Wavelet Networks

    • Hartmut Heinrich, Hartmut Dickhaus
    Pages 183-199

  11. Detection of Evoked Potentials

    • Peter Husar
    Pages 201-220

  12. Visual Evoked Potential Analysis Using Adaptive Chirplet Transform

    • Jie Cui, Willy Wong
    Pages 221-244

  13. Uterine EMG Analysis: Time-Frequency Based Techniques for Preterm Birth Detection

    • Mohamad Khalil, Marwa Chendeb, Mohamad Diab, Catherine Marque, Jacques Duchêne
    Pages 245-266

  14. Pattern Classification Techniques for EMG Signal Decomposition

    • Sarbast Rasheed, Daniel Stashuk
    Pages 267-289

  15. Parametric Modeling of Some Biosignals Using Optimization Metaheuristics

    • Amir Nakib, Amine Naït-Ali, Virginie Van Wassenhove, Patrick Siarry
    Pages 291-305

  16. Nonlinear Analysis of Physiological Time Series

    • Anisoara Paraschiv-Ionescu, Kamiar Aminian
    Pages 307-333

  17. Biomedical Data Processing Using HHT: A Review

    • Ming-Chya Wu, Norden E. Huang
    Pages 335-352

  18. Introduction to Multimodal Compression of Biomedical Data

    • Amine Naït-Ali, Emre Zeybek, Xavier Drouot
    Pages 353-374

  19. Back Matter

    Pages 375-378
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Keywords

About this book

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.

Editors and Affiliations

  • Labo. Images, Signaux et, Systèmes Intelligents (LISSI) EA 3956, Université Paris 12, Créteil, France

    Amine Naït-Ali

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