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dc.contributor.authorJiménez García, Jorge
dc.contributor.authorGarcía, María
dc.contributor.authorGutiérrez Tobal, Gonzalo César
dc.contributor.authorKheirandish Gozal, Leila
dc.contributor.authorVaquerizo Villar, Fernando 
dc.contributor.authorÁlvarez, Daniel
dc.contributor.authorCampo Matias, Félix del 
dc.contributor.authorGozal, David
dc.contributor.authorHornero Sánchez, Roberto 
dc.date.accessioned2022-09-23T12:32:21Z
dc.date.available2022-09-23T12:32:21Z
dc.date.issued2022
dc.identifier.citationComputers in Biology and Medicine, 2022, vol. 147, 105784es
dc.identifier.issn0010-4825es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/55620
dc.descriptionProducción Científicaes
dc.description.abstractThe gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%–90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.classificationObstructive sleep apneaes
dc.subject.classificationApnea obstructiva del sueñoes
dc.subject.classificationAirflowes
dc.subject.classificationFlujo aéreoes
dc.subject.classificationOximetryes
dc.subject.classificationOximetríaes
dc.titleA 2D convolutional neural network to detect sleep apnea in children using airflow and oximetryes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2022 The Authorses
dc.identifier.doi10.1016/j.compbiomed.2022.105784es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0010482522005510?via%3Dihubes
dc.peerreviewedSIes
dc.description.projectMinisterio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (project 10.13039/501100011033)es
dc.description.projectFondo Europeo de Desarrollo Regional - Unión Europea (projects PID2020-115468RB-I00 and PDC2021-120775-I00)es
dc.description.projectSociedad Española de Neumología y Cirugía Torácica (project 649/2018)es
dc.description.projectSociedad Española de Sueño (project Beca de Investigación SES 2019)es
dc.description.projectConsorcio Centro de Investigación Biomédica en Red - Instituto de Salud Carlos III - Ministerio de Ciencia, Innovación y Universidades (project CB19/01/00012)es
dc.description.projectNational Institutes of Health (projects HL083075, HL083129, UL1-RR-024134 and UL1 RR024989)es
dc.description.projectNational Heart, Lung, and Blood Institute (projects R24 HL114473 and 75N92019R002)es
dc.description.projectMinisterio de Educación, Cultura y Deporte (grant FPU16/02938)es
dc.description.projectMinisterio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC2019-028566-I)es
dc.description.projectNational Institutes of Health (grants HL130984, HL140548, and AG061824)es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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