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dc.contributor.authorSchetinin, Vitalyen
dc.contributor.authorJakaite, Livijaen
dc.date.accessioned2017-03-27T12:18:53Z
dc.date.available2017-03-27T12:18:53Z
dc.date.issued2017-03-21
dc.identifier.citationSchetinin V, Jakaite L (2017) 'Extraction of features from sleep EEG for Bayesian assessment of brain development', PLoS ONE, 12 (3).en
dc.identifier.issn1932-6203
dc.identifier.pmid28323852
dc.identifier.doi10.1371/journal.pone.0174027
dc.identifier.urihttp://hdl.handle.net/10547/622072
dc.description.abstractBrain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG). Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.
dc.description.sponsorshipLeverhulme Trusten
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.relation.urlhttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174027en
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360314/
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectfeature extractionen
dc.subjectdiscontinuityen
dc.subjectsegmentationen
dc.subjectEEG dataen
dc.subjectpredictive posterioren
dc.subjectBayesian learningen
dc.titleExtraction of features from sleep EEG for Bayesian assessment of brain developmenten
dc.typeArticleen
dc.identifier.journalPLoS ONEen
dc.identifier.pmcidPMC5360314
dc.date.updated2017-03-27T12:16:39Z
html.description.abstractBrain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG). Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.


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