title: Gaussian mixture models for classification of neonatal seizures using EEG.
abstract: A real-time neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. The system includes feature transformation techniques and classifier output postprocessing. The detector was evaluated on a database of 20 patients with 330 h of recordings. A detailed analysis of the choice of parameters for the detector is provided. A mean good detection rate of 79% was obtained with only 0.5 false detections per hour. A thorough review of all misclassified events was performed, from which a number of patterns causing false detections were identified.
authors.affiliation: Department Electrical and Electronic Engineering, University College Cork, Ireland. eoint@rennes.ucc.ie
authors.forename: E M
authors.initials: EM
authors.lastname: Thomas
authors.forename: A
authors.initials: A
authors.lastname: Temko
authors.forename: G
authors.initials: G
authors.lastname: Lightbody
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
authors.forename: G B
authors.initials: GB
authors.lastname: Boylan
mesh_terms: D016477:Artifacts; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007231:Infant, Newborn; D008959:Models, Neurological; D009068:Movement; D016011:Normal Distribution; D012372:ROC Curve; D012640:Seizures / Q000145:classification*; D012815:Signal Processing, Computer-Assisted