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Brain Advance Access originally published online on September 28, 2006
Brain 2007 130(2):314-333; doi:10.1093/brain/awl241
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© The Author (2006). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Review Articles

Seizure prediction: the long and winding road

Florian Mormann1, Ralph G. Andrzejak3, Christian E. Elger1 and Klaus Lehnertz1,2

1 Department of Epileptology, University of Bonn Bonn, Germany 2 Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn Bonn, Germany 3 Department of Technology, University Pompeu Fabra Barcelona, Spain

Correspondence to: Florian Mormann, MD, PhD, Division of Biology 216–76, California Institute of Technology, Pasadena, CA 91125, USA E-mail: fmormann{at}yahoo.de

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Key Words: seizure anticipation; algorithm; performance; statistical validation; methodology; guidelines

Received March 28, 2006. Revised July 1, 2006. Accepted August 10, 2006.


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