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dc.contributor.authorVaquerizo-Villar, Fernando
dc.contributor.authorÁlvarez, Daniel
dc.contributor.authorKheirandish-Gozal, Leila
dc.contributor.authorGutiérrez-Tobal, Gonzalo César
dc.contributor.authorBarroso-García, Verónica
dc.contributor.authorCrespo, Andrea
dc.contributor.authordel Campo, Félix
dc.contributor.authorGozal, David
dc.contributor.authorHornero, Roberto
dc.date.accessioned2025-12-04T10:29:17Z
dc.date.available2025-12-04T10:29:17Z
dc.date.issued2018-03
dc.identifier.citationComputer Methods and Programs in Biomedicine, Marzo 2018, vol. 156, p. 141-149es
dc.identifier.issn0169-2607es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/80298
dc.descriptionProducción Científicaes
dc.description.abstractBackground and objective The aim of this study was to assess the utility of bispectrum-based oximetry approaches as a complementary tool to traditional techniques in the screening of pediatric sleep apnea-hypopnea syndrome (SAHS). Methods 298 blood oxygen saturation (SpO2) signals from children ranging 0–13 years of age were recorded during overnight polysomnography (PSG). These recordings were divided into three severity groups according to the PSG-derived apnea hypopnea index (AHI): AHI < 5 events per hour (e/h), 5 ≤ AHI < 10 e/h, AHI ≥ 10 e/h. For each pediatric subject, anthropometric variables, 3% oxygen desaturation index (ODI3) and spectral features from power spectral density (PSD) and bispectrum were obtained. Then, the fast correlation-based filter (FCBF) was applied to select a subset of relevant features that may be complementary, excluding those that are redundant. The selected features fed a multiclass multi-layer perceptron (MLP) neural network to build a model to estimate the SAHS severity degrees. Results An optimum subset with features from all the proposed methodological approaches was obtained: variables from bispectrum, as well as PSD, ODI3, Age, and Sex. In the 3-class classification task, the MLP model trained with these features achieved an accuracy of 76.0% and a Cohen's kappa of 0.56 in an independent test set. Additionally, high accuracies were reached using the AHI cutoffs for diagnosis of moderate (AHI = 5 e/h) and severe (AHI = 10 e/h) SAHS: 81.3% and 85.3%, respectively. These results outperformed the diagnostic ability of a MLP model built without using bispectral features. Conclusions Our results suggest that bispectrum provides additional information to anthropometric variables, ODI3 and PSD regarding characterization of changes in the SpO2 signal caused by respiratory events. Thus, oximetry bispectrum can be a useful tool to provide complementary information for screening of moderate-to-severe pediatric SAHS.es
dc.format.mimetypeapplication/pdfes
dc.language.isospaes
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.subject.classificationSleep apnea-hypopnea syndrome (SAHS)es
dc.subject.classificationChildrenes
dc.subject.classificationOximetryes
dc.subject.classificationBispectrumes
dc.subject.classificationFeature selectiones
dc.subject.classificationFeature classificationes
dc.titleUtility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordingses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2017 Elsevier B.V. All rights reservedes
dc.identifier.doi10.1016/j.cmpb.2017.12.020es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0169260717306429?via%3Dihubes
dc.identifier.publicationfirstpage141es
dc.identifier.publicationlastpage149es
dc.identifier.publicationtitleComputer Methods and Programs in Biomedicinees
dc.identifier.publicationvolume156es
dc.peerreviewedSIes
dc.type.hasVersioninfo:eu-repo/semantics/draftes
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco3325 Tecnología de las Telecomunicacioneses
dc.subject.unesco3314 Tecnología Médicaes


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