Ahmed, Rehan,Temko, Andriy,Marnane, William P.,Boylan, Geraldine,Lightbody, Gordon,

2017

January

Computers In Biology and Medicine

Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel

Published

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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.

0010-4825

//www.sciencedirect.com/science/article/pii/S0010482517300215

http://dx.doi.org/10.1016/j.compbiomed.2017.01.017