title: Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.
abstract: Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).
authors.affiliation: Department of Clinical Neurophysiology, MUMC+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands. guybogaarts@gmail.com.
authors.forename: J G
authors.initials: JG
authors.lastname: Bogaarts
authors.affiliation: Department of Clinical Neurophysiology, MUMC+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
authors.forename: D M W
authors.initials: DM
authors.lastname: Hilkman
authors.affiliation: Department of Clinical Neurophysiology, MUMC+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
authors.forename: E D
authors.initials: ED
authors.lastname: Gommer
authors.affiliation: Department of Clinical Neurophysiology, MUMC+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
authors.forename: V H J M
authors.initials: VH
authors.lastname: van Kranen-Mastenbroek
authors.affiliation: Department of Clinical Neurophysiology, MUMC+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
authors.forename: J P H
authors.initials: JP
authors.lastname: Reulen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine
keywords: EEG; Feature normalization; PCA; SVM; Seizure detection