Toward a personalized real-time diagnosis in neonatal seizure detection

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dc.contributor.authorTemko, Andriy
dc.contributor.authorSarkar, Achintya K. R.
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.authorMathieson, Sean
dc.contributor.authorMarnane, William P.
dc.contributor.authorLightbody, Gordon
dc.contributor.funderScience Foundation Ireland
dc.contributor.funderWellcome Trust
dc.date.accessioned2017-12-08T13:33:43Z
dc.date.available2017-12-08T13:33:43Z
dc.date.issued2017-09-11
dc.description.abstractThe problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.en
dc.description.sponsorshipWellcome Trust (Strategic Translational Award (098983/Z/12; Seed Award in Science (200704/Z/16)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid2800414
dc.identifier.citationTemko, A., Sarkar, A. K., Boylan, G. B., Mathieson, S., Marnane, W. P. and Lightbody, G. (2017) 'Toward a personalized real-time diagnosis in neonatal seizure detection', IEEE Journal of Translational Engineering in Health and Medicine, 5, 2800414 (14pp). doi: 10.1109/JTEHM.2017.2737992en
dc.identifier.doi10.1109/JTEHM.2017.2737992
dc.identifier.endpage14
dc.identifier.issn2168-2372
dc.identifier.journaltitleIEEE Journal of Translational Engineering in Health and Medicineen
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/5141
dc.identifier.volume5
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/
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]./
dc.relation.urihttp://ieeexplore.ieee.org/document/8031337/
dc.rightsThis work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/en
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.subjectNeonatalen
dc.subjectSeizureen
dc.subjectDetectionen
dc.subjectOnline adaptationen
dc.titleToward a personalized real-time diagnosis in neonatal seizure detectionen
dc.typeArticle (peer-reviewed)en
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