2.50
Hdl Handle:
http://hdl.handle.net/10547/293079
Title:
Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms
Authors:
Jakaite, Livija; Schetinin, Vitaly ( 0000-0003-1826-0153 ) ; Maple, Carsten
Abstract:
Newborn brain maturity can be assessed by expert analysis of maturity-related patterns recognizable in polysomnograms. Since 36 weeks most of these patterns become recognizable in EEG exclusively, particularly, in EEG recorded via the two central-temporal channels. The use of such EEG recordings enables experts to minimize the disturbance of sleep, preparation time as well as the movement artifacts. We assume that the brain maturity of newborns aged 36 weeks and older can be automatically assessed from the 2-channel sleep EEG as accurately as by expert analysis of the full polysomnographic information. We use Bayesian inference to test this assumption and assist experts to obtain the full probabilistic information on the EEG assessments. The Bayesian methodology is feasibly implemented with Monte Carlo integration over areas of high posterior probability density, however the existing techniques tend to provide biased assessments in the absence of prior information required to explore a model space in detail within a reasonable time. In this paper we aim to use the posterior information about EEG features to reduce possible bias in the assessments. The performance of the proposed method is tested on a set of EEG recordings.
Affiliation:
University of Bedfordshire
Citation:
Jakaite, L., Schetinin, V. & Maple, C. (2012) 'Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms', Computational and Mathematical Methods in Medicine, 2012.
Journal:
Computational and Mathematical Methods in Medicine
Issue Date:
2012
URI:
http://hdl.handle.net/10547/293079
DOI:
10.1155/2012/629654
Additional Links:
http://www.hindawi.com/journals/cmmm/2012/629654/; http://www.hindawi.com/journals/cmmm/2012/629654/
Type:
Article
Language:
en
ISSN:
1748-670X; 1748-6718
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorJakaite, Livijaen_GB
dc.contributor.authorSchetinin, Vitalyen_GB
dc.contributor.authorMaple, Carstenen_GB
dc.date.accessioned2013-05-30T13:10:53Z-
dc.date.available2013-05-30T13:10:53Z-
dc.date.issued2012-
dc.identifier.citationJakaite, L., Schetinin, V. & Maple, C. (2012) 'Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms', Computational and Mathematical Methods in Medicine, 2012.en_GB
dc.identifier.issn1748-670X-
dc.identifier.issn1748-6718-
dc.identifier.doi10.1155/2012/629654-
dc.identifier.urihttp://hdl.handle.net/10547/293079-
dc.description.abstractNewborn brain maturity can be assessed by expert analysis of maturity-related patterns recognizable in polysomnograms. Since 36 weeks most of these patterns become recognizable in EEG exclusively, particularly, in EEG recorded via the two central-temporal channels. The use of such EEG recordings enables experts to minimize the disturbance of sleep, preparation time as well as the movement artifacts. We assume that the brain maturity of newborns aged 36 weeks and older can be automatically assessed from the 2-channel sleep EEG as accurately as by expert analysis of the full polysomnographic information. We use Bayesian inference to test this assumption and assist experts to obtain the full probabilistic information on the EEG assessments. The Bayesian methodology is feasibly implemented with Monte Carlo integration over areas of high posterior probability density, however the existing techniques tend to provide biased assessments in the absence of prior information required to explore a model space in detail within a reasonable time. In this paper we aim to use the posterior information about EEG features to reduce possible bias in the assessments. The performance of the proposed method is tested on a set of EEG recordings.en_GB
dc.language.isoenen
dc.relation.urlhttp://www.hindawi.com/journals/cmmm/2012/629654/en_GB
dc.relation.urlhttp://www.hindawi.com/journals/cmmm/2012/629654/-
dc.rightsArchived with thanks to Computational and Mathematical Methods in Medicineen_GB
dc.subjectG760 Machine Learningen_GB
dc.subjectelectroencephalogramen
dc.subjectelectroencephalographyen
dc.subjectnewborn brain maturityen
dc.subjectBayesian methodsen
dc.subjectEEG maturityen_GB
dc.titleBayesian assessment of newborn brain maturity from two-channel sleep electroencephalogramsen
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen_GB
dc.identifier.journalComputational and Mathematical Methods in Medicineen_GB
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