title: On the predictability of epileptic seizures.
abstract: OBJECTIVE
  An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches.
  
  
  METHODS
  We compared 30 measures in terms of their ability to distinguish between the interictal period and the pre-seizure period. After completely analyzing continuous inctracranial multi-channel recordings from five patients lasting over days, we used ROC curves to distinguish between the amplitude distributions of interictal and preictal time profiles calculated for the respective measures. We compared different evaluation schemes including channelwise and seizurewise analysis plus constant and adaptive reference levels. Particular emphasis was placed on statistical validity and significance.
  
  
  RESULTS
  Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhibited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 min before seizures. Linear measures were found to perform similar or better than non-linear measures.
  
  
  CONCLUSIONS
  Results provide statistically significant evidence for the existence of a preictal state. Based on our findings, the most promising approach for prospective seizure anticipation could be a combination of bivariate and univariate measures.
  
  
  CONCLUSIONS
  Many measures reported capable of seizure prediction in earlier studies are found to be insignificant in performance, which underlines the need for statistical validation in this field.
authors.affiliation: Department of Epileptology, University of Bonn, Sigmund-Freud-Strasse 25, 53105 Bonn, Germany. fmormann@yahoo.de
authors.forename: Florian
authors.initials: F
authors.lastname: Mormann
authors.forename: Thomas
authors.initials: T
authors.lastname: Kreuz
authors.forename: Christoph
authors.initials: C
authors.lastname: Rieke
authors.forename: Ralph G
authors.initials: RG
authors.lastname: Andrzejak
authors.forename: Alexander
authors.initials: A
authors.lastname: Kraskov
authors.forename: Peter
authors.initials: P
authors.lastname: David
authors.forename: Christian E
authors.initials: CE
authors.lastname: Elger
authors.forename: Klaus
authors.initials: K
authors.lastname: Lehnertz
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001931:Brain Mapping; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors