title: Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives.
abstract: This paper describes a knowledge-based system for the elimination of false positives in the automated detection of epileptogenic sharp transients in the EEG. The system makes comprehensive use of spatial and temporal context information available on 16 channels of EEG, EKG, EMG, and EOG. A knowledge-based implementation is used because of the ease with which it allows the contextual rules to be expressed and refined. The resulting system is shown to be capable of rejecting a wide variety of artifacts commonly found in EEG recordings, artifacts that cause numerous false positive detections in systems making less comprehensive use of context.
authors.forename: J R
authors.initials: JR
authors.lastname: Glover
authors.forename: N
authors.initials: N
authors.lastname: Raghavan
authors.forename: P Y
authors.initials: PY
authors.lastname: Ktonas
authors.forename: J D
authors.initials: JD
authors.lastname: Frost
mesh_terms: D001185:Artificial Intelligence; D004562:Electrocardiography; D004569:Electroencephalography; D004576:Electromyography; D004585:Electrooculography; D004828:Epilepsies, Partial / Q000175:diagnosis*; D005189:False Positive Reactions; D006801:Humans