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

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Date
2017-01-26
Authors
Ahmed, Rehan
Temko, Andriy
Marnane, William P.
Boylan, Geraldine B.
Lightbody, Gordon
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Elsevier
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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.
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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
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