title: Seizure prediction for therapeutic devices: A review.
abstract: Research in seizure prediction has come a long way since its debut almost 4 decades ago. Early studies suffered methodological caveats leading to overoptimistic results and lack of statistical significance. The publication of guidelines addressing mainly the question of performance evaluation and statistical validation in seizure prediction helped revising the status of the field. While many studies failed to prove that above chance prediction is possible by applying these guidelines, other studies were successful. Methods based on EEG analysis using linear and nonlinear measures were reportedly successful in detecting preictal changes and using them to predict seizures above chance. In this review, we present a selection of studies in seizure prediction published in the last decade. The studies were selected based on the validity of the methods and the statistical significance of performance results. These results varied between studies and many showed acceptable levels of sensitivity and specificity that could be appealing for therapeutic devices. The relatively large prediction horizon and early preictal changes reported in most studies suggest that seizure prediction may work better in closed loop seizure control devices rather than as seizure advisory devices. The emergence of a large database of annotated long-term EEG recordings should help prospective assessment of prediction methods. Some questions remain to be addressed before large clinical trials involving seizure prediction can be carried out.
authors.affiliation: Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Biomedical Engineering, Montréal, QC, Canada. Electronic address: kais.gadhoumi@mail.mcgill.ca.
authors.forename: Kais
authors.initials: K
authors.lastname: Gadhoumi
authors.affiliation: École De Technologie Supérieure, Département de Génie Électrique, Montréal, QC, Canada; Centre de recherches mathématiques, Montréal, QC, Canada; McGill University, Biomedical Engineering, Montréal, QC, Canada; U678, INSERM, Paris, France.
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Lina
authors.affiliation: University of Bonn, Department of Epileptology, Bonn, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Mormann
authors.affiliation: Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Biomedical Engineering, Montréal, QC, Canada.
authors.forename: Jean
authors.initials: J
authors.lastname: Gotman
mesh_terms: D000465:Algorithms; D000818:Animals; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000628:therapy*; D012680:Sensitivity and Specificity
keywords: Algorithms; Focal epilepsy; Intracerebral EEG; Seizure prediction; Statistical validation; Therapeutic devices