title: Seizure Forecasting and the Preictal State in Canine Epilepsy.
abstract: The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.
authors.affiliation: * Electrical and Computer Engineering, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA.
authors.forename: Yogatheesan
authors.initials: Y
authors.lastname: Varatharajah
authors.affiliation: * Electrical and Computer Engineering, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA.
authors.forename: Ravishankar K
authors.initials: RK
authors.lastname: Iyer
authors.affiliation: † Department of Neurology and Physiology & Biomedical Engineering, Mayo Clinic Rochester, MN 55905, USA.
authors.forename: Brent M
authors.initials: BM
authors.lastname: Berry
authors.affiliation: † Department of Neurology and Physiology & Biomedical Engineering, Mayo Clinic Rochester, MN 55905, USA.
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Worrell
authors.affiliation: † Department of Neurology and Physiology & Biomedical Engineering, Mayo Clinic Rochester, MN 55905, USA.
authors.forename: Benjamin H
authors.initials: BH
authors.lastname: Brinkmann
mesh_terms: D000818:Animals; D019540:Area Under Curve; D004195:Disease Models, Animal; D004285:Dogs; D000069280:Electrocorticography / Q000295:instrumentation / Q000379:methods*; D004567:Electrodes, Implanted; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology; D000069550:Machine Learning; D011379:Prognosis; D012372:ROC Curve; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D059015:Wireless Technology
keywords: Epilepsy; ambulatory EEG; intracranial EEG (iEEG); machine learning; seizure forecasting