Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction Of Epileptic Seizures?

Ying-Cheng Lai, Ivan Osorio, Mark G. Frei, Mary Ann F Harrison

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Epilepsy, a common neurological disorder, manifests with seizures that occur suddenly, cyclically but aperiodically, features that enhance its disabling power. This chapter reviews the results of correlation dimension analysis from an extensive clinical database-over 2,000 total hours of continuous ECoG from 20 subjects with epilepsy. It examines the sensitivity of the estimated dimension to properties such as the signal amplitude and autocorrelation and describes the effect of the embedding (necessary for estimating dimension from time series. Following this, it discusses the filtering method and performs surrogate data analysis. Based on this understanding, it elucidates that the correlation dimension has no predictive power for seizures. Furthermore, it describes the fundamental relation between the fractal dimension and the Lyapunov exponents, as given by the Kaplan-Yorke formula, and argues for the inability of Lyapunov exponents to predict seizures. It then reviews the control studies based on non-stationary dynamical systems modeled by discrete-time maps and by continuous-time flows, for which the behavior of the Lyapunov exponents is known a priori, to show that the exponents have no predictive or even detective power for characteristic system changes-even when only an exceedingly small amount of noise is present. Finally, it discusses the supporting results from analysis of clinical ECoG data.

Original languageEnglish (US)
Title of host publicationComputational Neuroscience in Epilepsy
PublisherElsevier Inc.
Pages471-495
Number of pages25
ISBN (Print)9780123736499
DOIs
StatePublished - 2008

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Epilepsy
Seizures
Fractals
Nervous System Diseases
Noise
Databases

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Lai, Y-C., Osorio, I., Frei, M. G., & Harrison, M. A. F. (2008). Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction Of Epileptic Seizures? In Computational Neuroscience in Epilepsy (pp. 471-495). Elsevier Inc.. https://doi.org/10.1016/B978-012373649-9.50032-6

Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction Of Epileptic Seizures? / Lai, Ying-Cheng; Osorio, Ivan; Frei, Mark G.; Harrison, Mary Ann F.

Computational Neuroscience in Epilepsy. Elsevier Inc., 2008. p. 471-495.

Research output: Chapter in Book/Report/Conference proceedingChapter

Lai, Y-C, Osorio, I, Frei, MG & Harrison, MAF 2008, Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction Of Epileptic Seizures? in Computational Neuroscience in Epilepsy. Elsevier Inc., pp. 471-495. https://doi.org/10.1016/B978-012373649-9.50032-6
Lai Y-C, Osorio I, Frei MG, Harrison MAF. Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction Of Epileptic Seizures? In Computational Neuroscience in Epilepsy. Elsevier Inc. 2008. p. 471-495 https://doi.org/10.1016/B978-012373649-9.50032-6
Lai, Ying-Cheng ; Osorio, Ivan ; Frei, Mark G. ; Harrison, Mary Ann F. / Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction Of Epileptic Seizures?. Computational Neuroscience in Epilepsy. Elsevier Inc., 2008. pp. 471-495
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