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

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dc.contributor.author Ahmed, Rehan
dc.contributor.author Temko, Andriy
dc.contributor.author Marnane, William P.
dc.contributor.author Boylan, Geraldine B.
dc.contributor.author Lightbody, Gordon
dc.date.accessioned 2017-01-31T15:21:14Z
dc.date.available 2017-01-31T15:21:14Z
dc.date.issued 2017-01-26
dc.identifier.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 en
dc.identifier.volume 82
dc.identifier.startpage 100
dc.identifier.endpage 110
dc.identifier.issn 0010-4825
dc.identifier.uri http://hdl.handle.net/10468/3545
dc.identifier.doi 10.1016/j.compbiomed.2017.01.017
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland (Principal Investigator Award (SFI 10/IN.1/B3036) and a Science Foundation Ireland Centres Programme Award (12/RC/2272)) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
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.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Automated neonatal seizure detection en
dc.subject Sequential classifier en
dc.subject Fusion en
dc.subject Gaussian dynamic time warping en
dc.title Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Rehan Ahmed, Electrical and Electronic Engineering, University College Cork, Ireland, T: +353 21 490 3000, E: rehan.ahmed@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this item is restricted until 12 months after publication by the request of the publisher. en
dc.check.date 2018-01-26
dc.description.version Accepted Version en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Computers in Biology and Medicine en
dc.internal.copyrightchecked !!CORA!! en
dc.internal.IRISemailaddress rehan.ahmed@ucc.ie en


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© 2016, Elsevier Inc. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2016, Elsevier Inc. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
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