A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Producción Científica

Detalhes bibliográficos
Autores: Jimenez García, Jorge, García Gadañón, María, Gutierrez Tobal, Gonzalo César, Kheirandish Gozal, Leila, Vaquerizo Villar, Fernando, Álvarez González, Daniel, Campo Matias, Félix del, Gozal, David, Hornero Sánchez, Roberto
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/55620
Acesso em linha:https://doi.org/10.1016/j.compbiomed.2022.105784
https://uvadoc.uva.es/handle/10324/55620
Access Level:acceso abierto
Palavra-chave:Obstructive sleep apnea
Apnea obstructiva del sueño
Airflow
Flujo aéreo
Oximetry
Oximetría
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spelling A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetryJimenez García, JorgeGarcía Gadañón, MaríaGutierrez Tobal, Gonzalo CésarKheirandish Gozal, LeilaVaquerizo Villar, FernandoÁlvarez González, DanielCampo Matias, Félix delGozal, DavidHornero Sánchez, RobertoObstructive sleep apneaApnea obstructiva del sueñoAirflowFlujo aéreoOximetryOximetríaProducción CientíficaThe 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.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (project 10.13039/501100011033)Fondo Europeo de Desarrollo Regional - Unión Europea (projects PID2020-115468RB-I00 and PDC2021-120775-I00)Sociedad Española de Neumología y Cirugía Torácica (project 649/2018)Sociedad Española de Sueño (project Beca de Investigación SES 2019)Consorcio Centro de Investigación Biomédica en Red - Instituto de Salud Carlos III - Ministerio de Ciencia, Innovación y Universidades (project CB19/01/00012)National Institutes of Health (projects HL083075, HL083129, UL1-RR-024134 and UL1 RR024989)National Heart, Lung, and Blood Institute (projects R24 HL114473 and 75N92019R002)Ministerio de Educación, Cultura y Deporte (grant FPU16/02938)Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC2019-028566-I)National Institutes of Health (grants HL130984, HL140548, and AG061824)Elsevier2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.compbiomed.2022.105784https://uvadoc.uva.es/handle/10324/55620reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.sciencedirect.com/science/article/pii/S0010482522005510?via%3Dihubinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/556202026-06-13T12:44:47Z
dc.title.none.fl_str_mv A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
title A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
spellingShingle A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
Jimenez García, Jorge
Obstructive sleep apnea
Apnea obstructiva del sueño
Airflow
Flujo aéreo
Oximetry
Oximetría
title_short A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
title_full A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
title_fullStr A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
title_full_unstemmed A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
title_sort A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
dc.creator.none.fl_str_mv Jimenez García, Jorge
García Gadañón, María
Gutierrez Tobal, Gonzalo César
Kheirandish Gozal, Leila
Vaquerizo Villar, Fernando
Álvarez González, Daniel
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
author Jimenez García, Jorge
author_facet Jimenez García, Jorge
García Gadañón, María
Gutierrez Tobal, Gonzalo César
Kheirandish Gozal, Leila
Vaquerizo Villar, Fernando
Álvarez González, Daniel
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
author_role author
author2 García Gadañón, María
Gutierrez Tobal, Gonzalo César
Kheirandish Gozal, Leila
Vaquerizo Villar, Fernando
Álvarez González, Daniel
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Obstructive sleep apnea
Apnea obstructiva del sueño
Airflow
Flujo aéreo
Oximetry
Oximetría
topic Obstructive sleep apnea
Apnea obstructiva del sueño
Airflow
Flujo aéreo
Oximetry
Oximetría
description Producción Científica
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.compbiomed.2022.105784
https://uvadoc.uva.es/handle/10324/55620
url https://doi.org/10.1016/j.compbiomed.2022.105784
https://uvadoc.uva.es/handle/10324/55620
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0010482522005510?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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