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)


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.


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