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

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dc.check.date2018-01-26
dc.check.infoAccess to this item is restricted until 12 months after publication by the request of the publisher.en
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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