dc.contributor.author |
Mathieson, Sean |
|
dc.contributor.author |
Rennie, Janet |
|
dc.contributor.author |
Livingstone, Vicki |
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dc.contributor.author |
Temko, Andriy |
|
dc.contributor.author |
Low, Evonne |
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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 |
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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 |
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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 |
|