title: Seizure prediction in epilepsy: from circadian concepts via probabilistic forecasting to statistical evaluation.
abstract: Seizure prediction performance is hampered by high numbers of false predictions. Here we present an approach to reduce the number of false predictions based on circadian concepts. Based on eight representative patients we demonstrate that this approach increases the performance considerably. The fraction of patients for whom we found a significant seizure prediction performance was increased from 25% to 38% by accounting for circadian dependencies.
authors.affiliation: Faculty of Mathematics and Physics, University of Freiburg, Freiburg, Germany. schelter@fdm.uni-freiburg.de
authors.forename: Björn
authors.initials: B
authors.lastname: Schelter
authors.forename: Hinnerk
authors.initials: H
authors.lastname: Feldwisch-Drentrup
authors.forename: Matthias
authors.initials: M
authors.lastname: Ihle
authors.forename: Andreas
authors.initials: A
authors.lastname: Schulze-Bonhage
authors.forename: Jens
authors.initials: J
authors.lastname: Timmer
mesh_terms: D000328:Adult; D000465:Algorithms; D002648:Child; D002940:Circadian Rhythm; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity