2024-03-28T20:40:11Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/217492021-06-23T13:29:00Zcom_10324_1191com_10324_931com_10324_894col_10324_1380
Álvarez González, Daniel
Gutiérrez Tobal, Gonzalo César
Vaquerizo Villar, Fernando
Barroso García, Verónica
Crespo Senado, Andrea
Arroyo Domingo, Carmen Ainhoa
Campo Matias, Félix del
Hornero Sánchez, Roberto
2016-12-15T07:34:27Z
2016-12-15T07:34:27Z
2016
Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2016 Global, Institute of Electrical and Electronics Engineers (IEEE) , 2016, p. 79-82
978-1-5090-2484-1
http://uvadoc.uva.es/handle/10324/21749
Producción Científica
Sleep 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)
application/pdf
eng
Institute of Electrical and Electronics Engineers (IEEE)
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 International
Oximetry
Automated Analysis of Unattended Portable Oximetry by means of Bayesian Neural Networks to Assist in the Diagnosis of Sleep Apnea
info:eu-repo/semantics/bookPart
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7500953