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dc.contributor.authorSchetinin, Vitalyen_GB
dc.contributor.authorJakaite, Livijaen_GB
dc.contributor.authorSchult, Joachimen_GB
dc.date.accessioned2013-04-07T16:28:18Z
dc.date.available2013-04-07T16:28:18Z
dc.date.issued2011
dc.identifier.citationSchetinin, V.; Jakaite, L.; Schult, J., (2011) 'Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation,' Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on: 1-6en_GB
dc.identifier.isbn9781457711893
dc.identifier.doi10.1109/CBMS.2011.5999111
dc.identifier.urihttp://hdl.handle.net/10547/279158
dc.description.abstractClinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment.
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5999111en_GB
dc.subjectbrain modelsen_GB
dc.subjectelectroencephalographyen_GB
dc.subjectBayesian methodsen_GB
dc.subjectnewborn brain maturityen_GB
dc.titleInformativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturationen
dc.typeConference papers, meetings and proceedingsen
html.description.abstractClinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment.


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