title: Robustness of time frequency distribution based features for automated neonatal EEG seizure detection.
abstract: In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93.
authors.forename: S B
authors.initials: SB
authors.lastname: Nagaraj
authors.forename: N J
authors.initials: NJ
authors.lastname: Stevenson
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
authors.forename: G B
authors.initials: GB
authors.lastname: Boylan
authors.forename: G
authors.initials: G
authors.lastname: Lightbody
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001331:Automation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis*; D012640:Seizures / Q000175:diagnosis*; D016008:Statistical Distributions; D013997:Time Factors