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Seizure Predictability in an Experimental Model of Epilepsy

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Data Mining in Biomedicine

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 7))

Abstract

We have previously reported preictal spatiotemporal transitions in human mesial temporal lobe epilepsy (MTLE) using short term Lyapunov exponent (STL max ) and average angular frequency (\( \Omega \) ). These results have prompted us to apply the quantitative nonlinear methods to a limbic epilepsy rat (CLE), as this model has several important features of human MTLE. The present study tests the hypothesis that preictal dynamical changes similar to those seen in human MTLE exist in the CLE model. Forty-two, 2-hr epoch data sets from 4 CLE rats (mean seizure duration 74±20 sec) are analyzed, each containing a focal onset seizure and intracranial data beginning 1 hr before the seizure onset. Three nonlinear measures, correlation integral, short-term largest Lyapunov exponent and average angular frequency are used in the current study. Data analyses show multiple transient drops in STL max values during the preictal period followed by a significant drop during the ictal period. Average angular frequency values demonstrate transient peaks during the preictal period followed by a significant peak during the ictal period. Convergence among electrode sites is also observed in both STL max and \( \Omega \) values before seizure onset. Results suggest that dynamical changes precede and accompany seizures in rat CLE. Thus, it may be possible to use the rat CLE model as a tool to refine and test real-time seizure prediction, and closed-loop intervention techniques.

This study was supported by NIH grant RO1EB002089, Children’s Miracle Network, University of Florida Division of Sponsored Research, and Department of Veterans Affairs.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Florida, Malcolm Randall Department of Veteran’s Affairs Medical Center, Gainesville, FL

    S. P. Nair

  2. Department of Neuroscience, University of Florida, Malcolm Randall Department of Veteran’s Affairs Medical Center, Gainesville, FL

    D. -S. Shiau

  3. Department of Biomedical Engineering, Arizona State University, Tempe, AZ

    L. D. Iasemidis

  4. Department of Pediatrics, University of Florida, Gainesville, FL

    W. M. Norman

  5. Department of Industrial and Systems Engineering, Computer and Information Science and Engineering, Biomedical Engineering, University of Florida, Gainesville, FL

    P. M. Pardalos

  6. Department of Neurology, Biomedical Engineering, Neuroscience, Pediatrics and Psychiatry, University of Florida, Malcolm Randall Department of Veteran’s Affairs Medical Center, Gainesville, FL

    J. C. Sackellares

  7. Department of Pediatrics, Neurology, Neuroscience, Biomedical Engineering, University of Florida, Gainesville, FL

    P. R. Carney

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  1. University of Florida, Gainesville, FL

    Panos M. Pardalos

  2. Florida State University, Tallahassee, FL

    Vladimir L. Boginski

  3. Dash Optimization, Englewood Cliffs, NJ

    Alkis Vazacopoulos

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Nair, S.P. et al. (2007). Seizure Predictability in an Experimental Model of Epilepsy. In: Pardalos, P.M., Boginski, V.L., Vazacopoulos, A. (eds) Data Mining in Biomedicine. Springer Optimization and Its Applications, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-69319-4_27

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