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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/21750

    Título
    Multi-Class AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome Severity from Oximetry Recordings Obtained at Home
    Autor
    Gutierrez Tobal, Gonzalo CésarAutoridad UVA Orcid
    Álvarez González, DanielAutoridad UVA Orcid
    Crespo Senado, Andrea
    Arroyo Domingo, Carmen AinhoaAutoridad UVA
    Vaquerizo Villar, FernandoAutoridad UVA Orcid
    Barroso García, VerónicaAutoridad UVA Orcid
    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. 95-99
    Resumen
    This paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<5 events/hour), mild-SAHS (5≤AHI<15 e/h), moderate-SAHS (15≤AHI<30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohen’s kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.
    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/10324/21750
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Capítulos de monografías [10]
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    Nombre:
    GutierrezTobal_etal_PAHCE_2016_post-review.pdf
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    Attribution-NonCommercial-NoDerivatives 4.0 InternationalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International

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