title: Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.
abstract: Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p<0.05) in 5/5 dogs analyzed.
authors.affiliation: Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, MN, United States of America.
authors.forename: Benjamin H
authors.initials: BH
authors.lastname: Brinkmann
authors.affiliation: Veterinary Medical Center, University of Minnesota, St. Paul, MN, United States of America.
authors.forename: Edward E
authors.initials: EE
authors.lastname: Patterson
authors.affiliation: School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
authors.forename: Charles
authors.initials: C
authors.lastname: Vite
authors.affiliation: Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, MN, United States of America.
authors.forename: Vincent M
authors.initials: VM
authors.lastname: Vasoli
authors.affiliation: Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, MN, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Crepeau
authors.affiliation: Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, MN, United States of America.
authors.forename: Matt
authors.initials: M
authors.lastname: Stead
authors.affiliation: NeuroVista Inc. Seattle, WA, United States of America.
authors.forename: J Jeffry
authors.initials: JJ
authors.lastname: Howbert
authors.affiliation: Department of Electrical and Computer Engineering, University of Minnesota, St. Paul, MN, United States of America.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Cherkassky
authors.affiliation: Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America.
authors.forename: Joost B
authors.initials: JB
authors.lastname: Wagenaar
authors.affiliation: Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America.
authors.forename: Brian
authors.initials: B
authors.lastname: Litt
authors.affiliation: Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, MN, United States of America.
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Worrell
mesh_terms: D000369:Aged, 80 and over; D000818:Animals; D004283:Dog Diseases / Q000503:physiopathology*; D004285:Dogs; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000662:veterinary*; D004827:Epilepsy / Q000503:physiopathology / Q000662:veterinary*; D005544:Forecasting; D006801:Humans; D023421:Models, Animal; D012372:ROC Curve; D060388:Support Vector Machine; D013686:Telemetry / Q000295:instrumentation / Q000379:methods