2026-04-29T10:58:33Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/217492025-02-20T11:44:12Zcom_10324_1191com_10324_931com_10324_894col_10324_1380

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 2016 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. application/pdf http://uvadoc.uva.es/handle/10324/21749 eng Institute of Electrical and Electronics Engineers (IEEE) 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 TEXT UVaDOC. Repositorio Documental de la Universidad de Valladolid Hispana