Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea

Producción Científica

Detalhes bibliográficos
Autores: Álvarez González, Daniel, Gutierrez Tobal, Gonzalo César, Vaquerizo Villar, Fernando, Barroso García, Verónica, Crespo Senado, Andrea, Arroyo Domingo, Carmen Ainhoa, Campo Matias, Félix del, Hornero Sánchez, Roberto
Formato: capítulo de livro
Fecha de publicación:2016
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/21749
Acesso em linha:http://uvadoc.uva.es/handle/10324/21749
Access Level:acceso abierto
Palavra-chave:Oximetry
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spelling Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apneaÁlvarez González, DanielGutierrez Tobal, Gonzalo CésarVaquerizo Villar, FernandoBarroso García, VerónicaCrespo Senado, AndreaArroyo Domingo, Carmen AinhoaCampo Matias, Félix delHornero Sánchez, RobertoOximetryProducción CientíficaSleep apnea-hypopnea syndrome (SAHS) is a chronic sleep-related breathing disorder, which is currently considered a major health problem. In-lab nocturnal polysomnography (NPSG) is the gold standard diagnostic technique though it is complex and relatively unavailable. On the other hand, the analysis of blood oxygen saturation (SpO2) from nocturnal pulse oximetry (NPO) is a simple, noninvasive, highly available and effective alternative. This study focused on the design and assessment of a neural network (NN) aimed at detecting SAHS using information from at-home unsupervised portable SpO2 recordings. A Bayesian multilayer perceptron NN (MLP-NN) was proposed, fed with complementary oximetric features properly selected. A dataset composed of 320 unattended SpO2 recordings was analyzed (60% for training and 40% for validation). The proposed Bayesian MLP-NN achieved 94.2% sensitivity, 69.6% specificity, and 89.8% accuracy in the test set. Our results suggest that automated analysis of at-home portable NPO recordings by means of Bayesian MLP-NN could be an effective and highly available technique in the context of SAHS diagnosis.Junta de Castilla y León (project VA059U13)Pneumology and Thoracic Surgery Spanish Society (265/2012)Institute of Electrical and Electronics Engineers (IEEE)2016info:eu-repo/semantics/bookPartapplication/pdfhttp://uvadoc.uva.es/handle/10324/21749reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7500953info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/217492026-06-13T12:44:47Z
dc.title.none.fl_str_mv Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
title Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
spellingShingle Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
Álvarez González, Daniel
Oximetry
title_short Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
title_full Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
title_fullStr Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
title_full_unstemmed Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
title_sort Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
dc.creator.none.fl_str_mv Álvarez González, Daniel
Gutierrez Tobal, Gonzalo César
Vaquerizo Villar, Fernando
Barroso García, Verónica
Crespo Senado, Andrea
Arroyo Domingo, Carmen Ainhoa
Campo Matias, Félix del
Hornero Sánchez, Roberto
author Álvarez González, Daniel
author_facet Álvarez González, Daniel
Gutierrez Tobal, Gonzalo César
Vaquerizo Villar, Fernando
Barroso García, Verónica
Crespo Senado, Andrea
Arroyo Domingo, Carmen Ainhoa
Campo Matias, Félix del
Hornero Sánchez, Roberto
author_role author
author2 Gutierrez Tobal, Gonzalo César
Vaquerizo Villar, Fernando
Barroso García, Verónica
Crespo Senado, Andrea
Arroyo Domingo, Carmen Ainhoa
Campo Matias, Félix del
Hornero Sánchez, Roberto
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Oximetry
topic Oximetry
description Producción Científica
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv http://uvadoc.uva.es/handle/10324/21749
url http://uvadoc.uva.es/handle/10324/21749
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7500953
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 Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
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 15.228081

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