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Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel
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Accepted Version
Date
2017-01-26
Authors
Ahmed, Rehan
Temko, Andriy
Marnane, William P.
Boylan, Geraldine B.
Lightbody, Gordon
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
Abstract
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.
Description
Keywords
Automated neonatal seizure detection , Sequential classifier , Fusion , Gaussian dynamic time warping
Citation
Ahmed, R., Temko, A., Marnane, W. P., Boylan, G. and Lightbody, G. (2017) 'Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel', Computers in Biology and Medicine, 82, pp. 100-110. doi:10.1016/j.compbiomed.2017.01.017