Chaotic Time Series Analysis Using Short and Noisy Data Sets: Application to a Clinical Epilepsy Seizure

  • G. W. Frank
  • T. Lookman
  • M. A. H. Nerenberg
Part of the NATO ASI Series book series (NSSB, volume 208)

Abstract

In recent years the methods of chaotic time series analysis have been applied to data sets from a number of experimental systems. In this note we report on work in progress on the usefulness of chaotic time series analysis as a potential diagnostic tool in the classification of epileptic seizure activity [1]. Seizure episodes are classified by correlation dimension and estimated largest Lyapunov exponent. The exponent is found using a modified version of Wolf’s method [2]. We begin with a brief discussion of some of these modifications before proceeding to a discussion of their application to an epilepsy data set.

Keywords

Lyapunov Exponent Correlation Dimension Large Lyapunov Exponent Seizure Event Univariate Time Series 
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

© Plenum Press, New York 1989

Authors and Affiliations

  • G. W. Frank
    • 1
  • T. Lookman
    • 1
  • M. A. H. Nerenberg
    • 1
  1. 1.Department of Applied MathematicsUniversity of Western OntarioLondonCanada

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