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Quiet sleep detection in preterm infants using deep convolutional neural networks

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dc.creatorAnsari A.H.spa
dc.creatorDe Wel O.spa
dc.creatorLavanga M.spa
dc.creatorCaicedo A.spa
dc.creatorDereymaeker A.spa
dc.creatorJansen K.spa
dc.creatorVervisch J.spa
dc.creatorDe Vos M.spa
dc.creatorNaulaers G.spa
dc.creatorVan Huffel S.spa
dc.date.accessioned2020-05-25T23:56:22Z
dc.date.available2020-05-25T23:56:22Z
dc.date.created2018spa
dc.description.abstractObjective. Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. Approach. In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. Main results. The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. Significance. Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants. © 2018 IOP Publishing Ltd.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1088/1741-2552/aadc1f
dc.identifier.issn17412560
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22407
dc.language.isoengspa
dc.publisherInstitute of Physics Publishingspa
dc.relation.citationIssueNo. 6
dc.relation.citationTitleJournal of Neural Engineering
dc.relation.citationVolumeVol. 15
dc.relation.ispartofJournal of Neural Engineering, ISSN:17412560, Vol.15, No.6 (2018)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85056640275&doi=10.1088%2f1741-2552%2faadc1f&partnerID=40&md5=e8c4d90292f0be7dafb243e04340c93fspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordArticlespa
dc.subject.keywordNewborneng
dc.subject.keywordBrain maturationspa
dc.subject.keywordClassificationspa
dc.subject.keywordClinical articlespa
dc.subject.keywordConvolutional neural networkspa
dc.subject.keywordCorrelation analysisspa
dc.subject.keywordElectroencephalogramspa
dc.subject.keywordElectroencephalographyspa
dc.subject.keywordFeature extractionspa
dc.subject.keywordHumanspa
dc.subject.keywordInfantspa
dc.subject.keywordMachine learningspa
dc.subject.keywordNerve cell differentiationspa
dc.subject.keywordNewborn carespa
dc.subject.keywordPrematurityspa
dc.subject.keywordPriority journalspa
dc.subject.keywordReceiver operating characteristicspa
dc.subject.keywordSleepspa
dc.subject.keywordSleep stagespa
dc.subject.keywordAlgorithmspa
dc.subject.keywordArtificial neural networkspa
dc.subject.keywordAutomationspa
dc.subject.keywordBrainspa
dc.subject.keywordFemalespa
dc.subject.keywordGrowtheng
dc.subject.keywordMalespa
dc.subject.keywordNewbornspa
dc.subject.keywordPhysiologyspa
dc.subject.keywordPrematurityspa
dc.subject.keywordProceduresspa
dc.subject.keywordSleepspa
dc.subject.keywordWakefulnessspa
dc.subject.keywordAlgorithmsspa
dc.subject.keywordAutomationspa
dc.subject.keywordBrainspa
dc.subject.keywordElectroencephalographyspa
dc.subject.keywordFemalespa
dc.subject.keywordHumansspa
dc.subject.keywordInfanteng
dc.subject.keywordInfanteng
dc.subject.keywordMalespa
dc.subject.keywordNeural Networks (Computer)spa
dc.subject.keywordSleepspa
dc.subject.keywordSleep Stagesspa
dc.subject.keywordWakefulnessspa
dc.subject.keywordConvolutional neural networkspa
dc.subject.keywordEEGspa
dc.subject.keywordPreterm neonatespa
dc.subject.keywordSleep stage classificationspa
dc.titleQuiet sleep detection in preterm infants using deep convolutional neural networksspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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