title: Speech recognition features for EEG signal description in detection of neonatal seizures.
abstract: In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.
authors.affiliation: Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
authors.forename: A
authors.initials: A
authors.lastname: Temko
authors.forename: G
authors.initials: G
authors.lastname: Boylan
authors.forename: W
authors.initials: W
authors.lastname: Marnane
authors.forename: G
authors.initials: G
authors.lastname: Lightbody
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D020936:Epilepsy, Benign Neonatal / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013068:Speech Production Measurement / Q000379:methods*