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Detection of Events in Signals Via the Model-Based Fault Diagnosis Approach: Application to Bio-Electrical Signals

  • Dominique Sauter
  • Thierry Cecchin
  • David Brie
Chapter

Abstract

Detection and extraction of a particular event is a very common problem in bio-electrical signal processing and can be solved by using the classical model-based fault diagnosis methods. From a statistical point of view, the addressed problem can be seen as a change detection problem when transformed into a stochastic framework where the output is categorical: model change from M θ0 to M θ1 . The purpose of this chapter is to present some detection methods based on a two-stage strategies: residual generation and evaluation. The first part is devoted to a theoretical background presentation of such methods with a special attention to robustness problems. In the second part, three different applications of bio-electrical signal processing are reported.

Keywords

Residual Evaluation Generalize Likelihood Ratio Test Generalize Likelihood Ratio Auto Regressive Residual Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Dominique Sauter
  • Thierry Cecchin
  • David Brie

There are no affiliations available

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