Skip navigation
Por favor, use este identificador para citar o enlazar este ítem:
Título: Automated Analysis of Unattended Portable Oximetry by means of Bayesian Neural Networks to Assist in the Diagnosis of Sleep Apnea
Autor: Álvarez, Daniel
Gutiérrez Tobal, Gonzalo César
Vaquerizo Villar, Fernando
Barroso García, Verónica
Crespo Senado, A.
Arroyo, C. A.
Campo Matías, Félix del
Hornero Sánchez, Roberto
Año del Documento: 2016
Editorial: Institute of Electrical and Electronics Engineers (IEEE)
Descripción: Producción Científica
Documento Fuente: Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2016 Global, Institute of Electrical and Electronics Engineers (IEEE) , 2016, p. 79-82
Resumen: 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.
Materias (normalizadas): Oximetry
ISBN: 978-1-5090-2484-1
Patrocinador: Junta de Castilla y León (project VA059U13)
Pneumology and Thoracic Surgery Spanish Society (265/2012)
Version del Editor:
Idioma: eng
Derechos: info:eu-repo/semantics/openAccess
Aparece en las colecciones:DEP71 - Capítulos de monografías

Ficheros en este ítem:
Fichero Descripción TamañoFormato 
Alvarez-etal_PAHCE_2016_post-review.pdf297,5 kBAdobe PDFThumbnail

Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons

Universidad de Valladolid
Powered by MIT's. DSpace software, Version 5.5