An Online Method for Detecting Nonlinearity Within a Signal

  • Beth Jelfs
  • Phebe Vayanos
  • Mo Chen
  • Su Lee Goh
  • Christos Boukis
  • Temujin Gautama
  • Tomasz Rutkowski
  • Tony Kuh
  • Danilo Mandic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Beth Jelfs
    • 1
  • Phebe Vayanos
    • 1
  • Mo Chen
    • 1
  • Su Lee Goh
    • 1
  • Christos Boukis
    • 2
  • Temujin Gautama
    • 3
  • Tomasz Rutkowski
    • 4
  • Tony Kuh
    • 5
  • Danilo Mandic
    • 1
  1. 1.Imperial College LondonUK
  2. 2.AITGreece
  3. 3.Phillips LeuvenBelgium
  4. 4.BSI RIKENJapan
  5. 5.University of Hawaii 

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