Chaotic Time Series Analysis Using Short and Noisy Data Sets: Application to a Clinical Epilepsy Seizure
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 . Seizure episodes are classified by correlation dimension and estimated largest Lyapunov exponent. The exponent is found using a modified version of Wolf’s method . We begin with a brief discussion of some of these modifications before proceeding to a discussion of their application to an epilepsy data set.
KeywordsLyapunov Exponent Correlation Dimension Large Lyapunov Exponent Seizure Event Univariate Time Series
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