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Complexity analysis of neonatal EEG using multiscale entropy: applications in brain maturation and sleep stage classification

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dc.creatorCaicedo Dorado, Alexander
dc.creatorJansen, Kspa
dc.creatorDereymaeker, Aspa
dc.creatorNaulaers, Gspa
dc.creatorVan Huffel, Sspa
dc.creatorLavanga, Mspa
dc.creatorDe Wel, Ospa
dc.date.accessioned2020-08-19T14:42:03Z
dc.date.available2020-08-19T14:42:03Z
dc.date.created2017-09-01spa
dc.description.abstractAutomated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using multiscale entropy. The aim of the current work is to investigate how EEG complexity evolves during electrocortical maturation and whether complexity features can be used to classify sleep stages. First , we developed a regression model that estimates the postmenstrual age (PMA) using a combination of complexity features. Then, these features are used to build a sleep stage classifier. The analysis is performed on a database consisting of 97 EEG recordings from 26 prematurely born infants, recorded between 27 and 42 weeks PMA. The results of the regression analysis revealed a significant positive correlation between the EEG complexity and the infant’s age. Moreover, the PMA of the neonate could be estimated with a root mean squared error of 1.88 weeks. The sleep stage classifier was able to discriminate quiet sleep from nonquiet sleep with an area under the curve (AUC) of 90%. These results suggest that the complexity of the brain dynamics is a highly useful index for brain maturation quantification and neonatal sleep stage classification.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.3390/e19100516
dc.identifier.issnISSN: 1099-4300
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/27401
dc.language.isoengspa
dc.publisherEntropyspa
dc.relation.citationEndPage516
dc.relation.citationIssueNo. 10
dc.relation.citationStartPage19
dc.relation.citationTitleEntropy
dc.relation.citationVolumeVol. 19
dc.relation.ispartofEntropy, ISSN: 1099-4300, Vol.19, No.10 (2017); pp. 19-516spa
dc.relation.urihttps://www.mdpi.com/1099-4300/19/10/516/htmspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.sourceEntropyspa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.keywordBrain maturationspa
dc.subject.keywordComplexityspa
dc.subject.keywordUltiscale entropyspa
dc.subject.keywordNeonatal EEGspa
dc.subject.keywordSleep stage classificationspa
dc.titleComplexity analysis of neonatal EEG using multiscale entropy: applications in brain maturation and sleep stage classificationspa
dc.title.TranslatedTitleAnálisis de la complejidad del EEG neonatal mediante entropía multiescala: aplicaciones en la maduración cerebral y la clasificación de las etapas del sueñospa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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