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

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dc.contributor.authorAhmed, Rehan
dc.contributor.authorTemko, Andriy
dc.contributor.authorMarnane, William P.
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.authorLightbody, Gordon
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2017-01-31T15:21:14Z
dc.date.available2017-01-31T15:21:14Z
dc.date.issued2017-01-26
dc.description.abstractSeizure 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.en
dc.description.sponsorshipScience Foundation Ireland (Principal Investigator Award (SFI 10/IN.1/B3036) and a Science Foundation Ireland Centres Programme Award (12/RC/2272))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAhmed, 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.2017.01.017en
dc.identifier.doi10.1016/j.compbiomed.2017.01.017
dc.identifier.endpage110
dc.identifier.issn0010-4825
dc.identifier.journaltitleComputers in Biology and Medicineen
dc.identifier.startpage100
dc.identifier.urihttps://hdl.handle.net/10468/3545
dc.identifier.volume82
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2016, Elsevier Inc. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectAutomated neonatal seizure detectionen
dc.subjectSequential classifieren
dc.subjectFusionen
dc.subjectGaussian dynamic time warpingen
dc.titleExploring temporal information in neonatal seizures using a dynamic time warping based SVM kernelen
dc.typeArticle (peer-reviewed)en
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