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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/66163

    Título
    Prediction of the sleep apnea severity using 2D-convolutional Neural Networks and respiratory effort signals
    Autor
    Barroso García, VerónicaAutoridad UVA Orcid
    Fernández Poyatos, Marta
    Sahelices Fernández, BenjamínAutoridad UVA Orcid
    Álvarez González, DanielAutoridad UVA Orcid
    Gozal, David
    Hornero Sánchez, RobertoAutoridad UVA Orcid
    Gutierrez Tobal, Gonzalo CésarAutoridad UVA Orcid
    Año del Documento
    2023
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Diagnostics 2023, 13(20), 3187
    Resumen
    The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC)=0.75 and a root mean square error (RMSE)=10.33 events/h when estimating the apnea-hypopnea index, and ICC=0.83 and RMSE=0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.
    Palabras Clave
    central sleep apnea
    obstructive sleep apnea
    abdominal respiratory signal
    thoracic respiratory signal
    convolutional neural network
    deep learning
    ISSN
    2075-4418
    Revisión por pares
    SI
    DOI
    10.3390/diagnostics13203187
    Patrocinador
    National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 (University of California, Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL53938 (University of Arizona), U01HL53940 (University of Washington), U01HL53941 (Boston University), and U01HL63463 (Case Western Reserve University). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002)
    Version del Editor
    https://www.mdpi.com/2075-4418/13/20/3187
    Idioma
    spa
    URI
    https://uvadoc.uva.es/handle/10324/66163
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • GCME- Artículos de revista [57]
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    Nombre:
    diagnostics-13-03187.pdf
    Tamaño:
    2.597Mb
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