Validation of an automated seizure detection algorithm for term neonates

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dc.contributor.authorMathieson, Sean R.
dc.contributor.authorStevenson, Nathan J.
dc.contributor.authorLow, Evonne
dc.contributor.authorMarnane, William P.
dc.contributor.authorRennie, Janet M.
dc.contributor.authorTemko, Andriy
dc.contributor.authorLightbody, Gordon
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.funderWellcome Trusten
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-10-26T07:23:29Z
dc.date.available2019-10-26T07:23:29Z
dc.date.issued2015-05-09
dc.description.abstractObjective: The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods: EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results: Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion: The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance: The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMathieson, S. R., Stevenson, N. J., Low, E., Marnane, W. P., Rennie, J. M., Temko, A., Lightbody, G. and Boylan, G. B. (2016), 'Validation of an automated seizure detection algorithm for term neonates', Clinical Neurophysiology, 127(1), pp. 156-168. DOI: 10.1016/j.clinph.2015.04.075en
dc.identifier.doi10.1016/j.clinph.2015.04.075en
dc.identifier.eissn1872-8952
dc.identifier.endpage168en
dc.identifier.issn1388-2457
dc.identifier.issued1en
dc.identifier.journaltitleClinical Neurophysiologyen
dc.identifier.startpage156en
dc.identifier.urihttps://hdl.handle.net/10468/8884
dc.identifier.volume127en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.projectinfo:eu-repo/grantAgreement/WT/Innovations/098983//Multicentre Clinical evaluation of a neonatal seizure detection algorithm./en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/10/IN.1/B3036/IE/Pattern RecognitIon Systems for continuous neurological Monitoring in NEOnates [NEOPRISM]./en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/en
dc.rights©2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. This is anopen access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectNeonatal seizuresen
dc.subjectAutomated seizure detectionen
dc.subjectNeonatal EEGen
dc.subjectHypoxic-ischaemic encephalopathyen
dc.subjectNeonatal neurologyen
dc.titleValidation of an automated seizure detection algorithm for term neonatesen
dc.typeArticle (peer-reviewed)en
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