In-depth performance analysis of an EEG based neonatal seizure detection algorithm

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dc.contributor.author Mathieson, Sean
dc.contributor.author Rennie, Janet
dc.contributor.author Livingstone, Vicki
dc.contributor.author Temko, Andriy
dc.contributor.author Low, Evonne
dc.contributor.author Pressler, R. M.
dc.contributor.author Boylan, Geraldine B.
dc.date.accessioned 2019-10-26T07:24:55Z
dc.date.available 2019-10-26T07:24:55Z
dc.date.issued 2016-02-21
dc.identifier.citation Mathieson, S., Rennie, J., Livingstone, V., Temko, A., Low, E., Pressler, R. M. and Boylan, G. B. (2016), 'In-depth performance analysis of an EEG based neonatal seizure detection algorithm', Clinical Neurophysiology, 127(5), pp. 2246-2256. DOI: 10.1016/j.clinph.2016.01.026 en
dc.identifier.volume 127 en
dc.identifier.issued 5 en
dc.identifier.startpage 2246 en
dc.identifier.endpage 2256 en
dc.identifier.issn 1388-2457
dc.identifier.uri http://hdl.handle.net/10468/8885
dc.identifier.doi 10.1016/j.clinph.2016.01.026 en
dc.description.abstract Objective: To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. Methods: EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure). Seizures were annotated by an expert and characterized using a novel set of 10 criteria. ANSeR seizure detection algorithm (SDA) seizure annotations were compared to the expert to derive detected and non-detected seizures at three SDA sensitivity thresholds. Differences in seizure characteristics between groups were compared using univariate and multivariate analysis. False detections were characterized. Results: The expert detected 421 seizures. The SDA at thresholds 0.4, 0.5, 0.6 detected 60%, 54% and 45% of seizures. At all thresholds, multivariate analyses demonstrated that the odds of detecting seizure increased with 4 criteria: seizure amplitude, duration, rhythmicity and number of EEG channels involved at seizure peak. Major causes of false detections included respiration and sweat artefacts or a highly rhythmic background, often during intermediate sleep. Conclusion: This rigorous analysis allows estimation of how key seizure features are exploited by SDAs. Significance: This study resulted in a beta version of ANSeR with significantly improved performance. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S1388245716000705?via%3Dihub
dc.rights © 2016, International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Automated seizure detection en
dc.subject Neonatal seizures en
dc.subject Detection algorithm en
dc.title In-depth performance analysis of an EEG based neonatal seizure detection algorithm en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Geraldine Boylan, Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland. +353-21-490-3000 Email:g.boylan@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Wellcome Trust en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Clinical Neurophysiology en
dc.internal.IRISemailaddress g.boylan@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/WT/Innovations/098983//Multicentre Clinical evaluation of a neonatal seizure detection algorithm./ en
dc.relation.project info: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.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/ en
dc.identifier.eissn 1872-8952


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© 2016, International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) Except where otherwise noted, this item's license is described as © 2016, International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
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