title: Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns.
abstract: Epileptic seizures can cause a variety of temporary changes in perception and behavior. In the human EEG they are reflected by multiple ictal patterns, where epileptic seizures typically become apparent as characteristic, usually rhythmic signals, often coinciding with or even preceding the earliest observable changes in behavior. Their detection at the earliest observable onset of ictal patterns in the EEG can, thus, be used to start more-detailed diagnostic procedures during seizures and to differentiate epileptic seizures from other conditions with seizure-like symptoms. Recently, warning and intervention systems triggered by the detection of ictal EEG patterns have attracted increasing interest. Since the workload involved in the detection of seizures by human experts is quite formidable, several attempts have been made to develop automatic seizure detection systems. So far, however, none of these found widespread application. Here, we present a novel procedure for generic, online, and real-time automatic detection of multimorphologic ictal-patterns in the human long-term EEG and its validation in continuous, routine clinical EEG recordings from 57 patients with a duration of approximately 43 hours and additional 1,360 hours of seizure-free EEG data for the estimation of the false alarm rates. We analyzed 91 seizures (37 focal, 54 secondarily generalized) representing the six most common ictal morphologies (alpha, beta, theta, and delta- rhythmic activity, amplitude depression, and polyspikes). We found that taking the seizure morphology into account plays a crucial role in increasing the detection performance of the system. Moreover, besides enabling a reliable (mean false alarm rate<0.5/h, for specific ictal morphologies<0.25/h), early and accurate detection (average correct detection rate>96%) within the first few seconds of ictal patterns in the EEG, this procedure facilitates the automatic categorization of the prevalent seizure morphologies without the necessity to adapt the proposed system to specific patients.
authors.affiliation: Neurobiology and Biophysics, Faculty of Biology, Albert-Ludwigs-University, Epilepsy Center, University Hospital, Freiburg, Germany. meier@biologie.uni-freiburg.de
authors.forename: Ralph
authors.initials: R
authors.lastname: Meier
authors.forename: Heike
authors.initials: H
authors.lastname: Dittrich
authors.forename: Andreas
authors.initials: A
authors.lastname: Schulze-Bonhage
authors.forename: Ad
authors.initials: A
authors.lastname: Aertsen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity