Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel.
Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is validated on a large dataset of EEG recordings from 17 neonates. The obtained results show an increase in the detection rate at very low false detections per hour, particularly achieving a 12% improvement in the detection of short seizure events over the static RBF kernel based system.
Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronic Engineering, University College Cork, Ireland. Electronic address: rehan@eleceng.ucc.ie.
Rehan
R
Ahmed
Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronic Engineering, University College Cork, Ireland.
Andriy
A
Temko
Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronic Engineering, University College Cork, Ireland.
William P
WP
Marnane
Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Pediatrics and Child Health, University College Cork, Ireland.
Geraldine
G
Boylan
Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronic Engineering, University College Cork, Ireland.
Gordon
G
Lightbody
D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D020936:Epilepsy, Benign Neonatal / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D060388:Support Vector Machine
Automated neonatal seizure detection; Fusion; Gaussian dynamic time warping; Sequential classifier