title: Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction.
abstract: Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory.
authors.affiliation: FDM, Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstr. 1, 79104 Freiburg, Germany. schelter@fdm.uni-freiburg.de
authors.forename: Björn
authors.initials: B
authors.lastname: Schelter
authors.forename: Matthias
authors.initials: M
authors.lastname: Winterhalder
authors.forename: Thomas
authors.initials: T
authors.lastname: Maiwald
authors.forename: Armin
authors.initials: A
authors.lastname: Brandt
authors.forename: Ariane
authors.initials: A
authors.lastname: Schad
authors.forename: Andreas
authors.initials: A
authors.lastname: Schulze-Bonhage
authors.forename: Jens
authors.initials: J
authors.lastname: Timmer
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors