An online method for detecting nonlinearity within a signal

  • Authors:

  • Beth Jelfs;Phebe Vayanos;Mo Chen;Su Lee Goh;Christos Boukis;Temujin Gautama;Tomasz Rutkowski;Tony Kuh;Danilo Mandic

  • Affiliations:

  • Imperial College London, UK;Imperial College London, UK;Imperial College London, UK;Imperial College London, UK;AIT, Greece;Phillips Leuven, Belgium;BSI RIKEN, Japan;University of Hawaii;Imperial College London, UK

  • Venue:
  • KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
  • Year:
  • 2006

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Abstract

A novel method for online analysis of the changes in signal modality is proposed. This is achieved by tracking the dynamics of the mixing parameter within a hybrid filter rather than the actual filter performance. An implementation of the proposed hybrid filter using a combination of the Least Mean Square (LMS) and the Generalised Normalised Gradient Descent (GNGD) algorithms is analysed and the potential of such a scheme for tracking signal nonlinearity is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis. Biological applications of the approach have been illustrated on EEG data of epileptic patients.