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Título
Automated Analysis of Unattended Portable Oximetry by means of Bayesian Neural Networks to Assist in the Diagnosis of Sleep Apnea
Autor
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
Resumo
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)
Pneumology and Thoracic Surgery Spanish Society (265/2012)
Version del Editor
Idioma
eng
Derechos
openAccess
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