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Automated Seizure Prediction Algorithm and its Statistical Assessment: A Report from Ten Patients

In: Data Mining in Biomedicine

Author

Listed:
  • D. -S. Shiau

    (University of Florida, Malcolm Randall Department of Veteran’s Affairs Medical Center)

  • L. D. Iasemidis

    (Arizona State University)

  • M. C. K. Yang

    (University of Florida)

  • P. M. Pardalos

    (University of Florida)

  • P. R. Carney

    (University of Florida)

  • L. K. Dance

    (University of Florida, Malcolm Randall Department of Veteran’s Affairs Medical Center)

  • W. Chaovalitwongse

    (The State University of New Jersey)

  • J. C. Sackellares

    (University of Florida, Malcolm Randall Department of Veteran’s Affairs Medical Center)

Abstract

The ability to predict epileptic seizures well prior to their clinical onset provides promise for new diagnostic applications and novel approaches to seizure control. Several groups of investigators have reported that it may be possible to predict seizures based on the quantitative analysis of EEG signal characteristics. The objective of this chapter is first to report an automated seizure warning algorithm, and second to compare its performance with other, theoretically sound, statistical algorithms. The proposed automated seizure prediction algorithm (ASPA) consists of an optimization method for the selection of critical cortical sites using measures from nonlinear dynamics, and a novel method for the detection of preictal transitions using adaptive transition thresholds according to the current state of dynamical interactions among brain sites. Continuous long-term (mean 210 hours per patient) intracranial EEG recordings obtained from ten patients with intractable epilepsy (total of 130 recorded seizures) were analyzed to test the proposed algorithm. For each patient, the prediction ROC (receiver operating characteristic) curve, generated from ASPA, was compared with the ones from periodic and random prediction schemes. The results showed that the performance of ASPA is significantly superior to each naïve prediction method used (p-value

Suggested Citation

  • D. -S. Shiau & L. D. Iasemidis & M. C. K. Yang & P. M. Pardalos & P. R. Carney & L. K. Dance & W. Chaovalitwongse & J. C. Sackellares, 2007. "Automated Seizure Prediction Algorithm and its Statistical Assessment: A Report from Ten Patients," Springer Optimization and Its Applications, in: Panos M. Pardalos & Vladimir L. Boginski & Alkis Vazacopoulos (ed.), Data Mining in Biomedicine, pages 517-533, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-69319-4_26
    DOI: 10.1007/978-0-387-69319-4_26
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