title: Parallel artefact rejection for epileptiform activity detection in routine EEG.
abstract: The EEG signal is very often contaminated by electrical activity external to the brain. These artefacts make the accurate detection of epileptiform activity more difficult. A scheme developed to improve the detection of these artefacts (and hence epileptiform event detection) is introduced. A structure of parallel Support Vector Machine classifiers is assembled, one classifier tuned to perform the identification of epileptiform activity, the remainder trained for the detection of ocular and movement-related artefacts. This strategy enables an absolute reduction in false detection rate of 21.6% with the constraint of ensuring all epileptic events are recognized. Such a scheme is desirable given that sections of data which are heavily contaminated with artefact need not be excluded from analysis.
authors.affiliation: Department of Electrical Engineering, University College Cork, Ireland. danielkel@rennes.ucc.ie
authors.forename: D
authors.initials: D
authors.lastname: Kelleher
authors.forename: A
authors.initials: A
authors.lastname: Temko
authors.forename: S
authors.initials: S
authors.lastname: Orregan
authors.forename: D
authors.initials: D
authors.lastname: Nash
authors.forename: B
authors.initials: B
authors.lastname: McNamara
authors.forename: D
authors.initials: D
authors.lastname: Costello
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005189:False Positive Reactions; D006801:Humans