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dc.creatorDe Wel, Ofelie 
dc.creatorLavanga, Mario 
dc.creatorCaicedo Dorado, Alexander 
dc.creatorJansen, Katrien 
dc.creatorNaulaers, Gunnar 
dc.creatorVan Huffel, Sabine 
dc.date.accessioned2020-05-26T00:06:56Z
dc.date.available2020-05-26T00:06:56Z
dc.date.created2019
dc.identifier.issn10994300
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23944
dc.description.abstractEstablished sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate's cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant. © 2019 by the authors.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.relation.ispartofEntropy, ISSN:10994300, Vol.21, No.10 (2019)
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074055832&doi=10.3390%2fe21100936&partnerID=40&md5=cd50de8c297378f76b7f8b3e65edbd72
dc.titleDecomposition of a multiscale entropy tensor for sleep stage identification in preterm infants
dc.typearticle
dc.publisherMDPI AG
dc.subject.keywordCpd
dc.subject.keywordEeg
dc.subject.keywordMultiscale entropy
dc.subject.keywordPreterm neonate
dc.subject.keywordSleep staging
dc.subject.keywordTensor decomposition
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaArtículo
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doihttps://doi.org/10.3390/e21100936
dc.relation.citationIssueNo. 10
dc.relation.citationTitleEntropy
dc.relation.citationVolumeVol. 21
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR


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