An SVM-based system and its performance for detection of seizures in neonates.
This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and robustness of the system and their influence on performance is shown. The SVM-based system is evaluated on a large clinical dataset using several epoch-based and event based metrics and curves of performance are reported. Additionally, a new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics.
Temko
Andriy
A
Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland. andreyt@ucc.ie
Thomas
Eoin
E
Boylan
Geraldine
G
Marnane
William
W
Lightbody
Gordon
G
D000465:Algorithms; D001331:Automation; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D006801:Humans; D007231:Infant, Newborn; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted