title: EEG feature pre-processing for neonatal epileptic seizure detection.
abstract: Aim of our project is to further optimize neonatal seizure detection using support vector machine (SVM). First, a Kalman filter (KF) was used to filter both feature and classifier output time series in order to increase temporal precision. Second, EEG baseline feature correction (FBC) was introduced to reduce inter patient variability in feature distributions. The performance of the detection methods is evaluated on 54 multi channel routine EEG recordings from 39 both term and pre-term newborns. The area under the receiver operating characteristics curve (AUC) as well as sensitivity and specificity are used to evaluate the performance of the classification method. SVM without KF and FBC achieves an AUC of 0.767 (sensitivity 0.679, specificity 0.707). The highest AUC of 0.902 (sensitivity 0.801, specificity 0.831) is achieved on baseline corrected features with a Kalman smoother used for training data pre-processing and a KF used to filter the classifier output. Both FBC and KF significantly improve neonatal epileptic seizure detection. This paper introduces significant improvements for the state of the art SVM based neonatal epileptic seizure detection.
authors.affiliation: Department of Clinical Neurophysiology, AZM Maastricht, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands, guy.bogaarts@mumc.nl.
authors.forename: J G
authors.initials: JG
authors.lastname: Bogaarts
authors.forename: E D
authors.initials: ED
authors.lastname: Gommer
authors.forename: D M W
authors.initials: DM
authors.lastname: Hilkman
authors.forename: V H J M
authors.initials: VH
authors.lastname: van Kranen-Mastenbroek
authors.forename: J P H
authors.initials: JP
authors.lastname: Reulen
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted