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

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dc.contributor.authorMathieson, Sean
dc.contributor.authorRennie, Janet
dc.contributor.authorLivingstone, Vicki
dc.contributor.authorTemko, Andriy
dc.contributor.authorLow, Evonne
dc.contributor.authorPressler, R. M.
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.funderWellcome Trusten
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-10-26T07:24:55Z
dc.date.available2019-10-26T07:24:55Z
dc.date.issued2016-02-21
dc.description.abstractObjective: 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.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMathieson, 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.2016.01.026en
dc.identifier.doi10.1016/j.clinph.2016.01.026en
dc.identifier.eissn1872-8952
dc.identifier.endpage2256en
dc.identifier.issn1388-2457
dc.identifier.issued5en
dc.identifier.journaltitleClinical Neurophysiologyen
dc.identifier.startpage2246en
dc.identifier.urihttps://hdl.handle.net/10468/8885
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.relation.urihttps://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.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAutomated seizure detectionen
dc.subjectNeonatal seizuresen
dc.subjectDetection algorithmen
dc.titleIn-depth performance analysis of an EEG based neonatal seizure detection algorithmen
dc.typeArticle (peer-reviewed)en
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