• español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Ricerca

    Tutto UVaDOCArchiviData di pubblicazioneAutoriSoggettiTitoli

    My Account

    Login

    Estadísticas

    Ver Estadísticas de uso

    Compartir

    Mostra Item 
    •   UVaDOC Home
    • PRODUZIONE SCIENTIFICA
    • Departamentos
    • Dpto. Teoría de la Señal y Comunicaciones e Ingeniería Telemática
    • DEP71 - Capítulos de monografías
    • Mostra Item
    •   UVaDOC Home
    • PRODUZIONE SCIENTIFICA
    • Departamentos
    • Dpto. Teoría de la Señal y Comunicaciones e Ingeniería Telemática
    • DEP71 - Capítulos de monografías
    • Mostra Item
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis

    Citas

    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/21749

    Título
    Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea
    Autor
    Álvarez González, DanielAutoridad UVA Orcid
    Gutierrez Tobal, Gonzalo CésarAutoridad UVA Orcid
    Vaquerizo Villar, FernandoAutoridad UVA Orcid
    Barroso García, VerónicaAutoridad UVA Orcid
    Crespo Senado, Andrea
    Arroyo Domingo, Carmen AinhoaAutoridad UVA
    Campo Matias, Félix delAutoridad UVA Orcid
    Hornero Sánchez, RobertoAutoridad UVA Orcid
    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
    Abstract
    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
    https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7500953
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/21749
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Capítulos de monografías [10]
    Mostra tutti i dati dell'item
    Files in questo item
    Nombre:
    Alvarez-etal_PAHCE_2016_post-review.pdf
    Tamaño:
    297.4Kb
    Formato:
    Adobe PDF
    Thumbnail
    Mostra/Apri
    Attribution-NonCommercial-NoDerivatives 4.0 InternationalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International

    Universidad de Valladolid

    Powered by MIT's. DSpace software, Version 5.10