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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/74125

    Título
    A convolutional neural network architecture to enhance oximetry ability to diagnose pediatric obstructive sleep apnea
    Autor
    Vaquerizo Villar, FernandoAutoridad UVA Orcid
    Álvarez González, DanielAutoridad UVA Orcid
    Kheirandish-Gozal, Leila
    Gutierrez Tobal, Gonzalo CésarAutoridad UVA Orcid
    Barroso García, VerónicaAutoridad UVA Orcid
    SantaMaría Vazquez, EduardoAutoridad UVA
    Campo Matias, Félix delAutoridad UVA Orcid
    Gozal, David
    Hornero Sánchez, RobertoAutoridad UVA Orcid
    Año del Documento
    2021
    Editorial
    IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC
    Descripción
    Producción Científica
    Documento Fuente
    IEEE Journal of Biomedical and Health Informatics, Agosto 2021, vol. 25, n. 8. p. 2906-2916.
    Zusammenfassung
    This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen’s kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.
    ISSN
    2168-2194
    Revisión por pares
    SI
    DOI
    10.1109/JBHI.2020.3048901
    Patrocinador
    This work was supported by 'Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación’ and ‘European Regional Development Fund (FEDER)’ under projects DPI2017-84280-R and RTC 2017 6516-1, by “European Commission” and “FEDER” under project 'Análisis y correlación entre la epigenética y la actividad cerebral para evaluar el riesgo de migraña crónica y episódica en mujeres' (‘Cooperation Programme Interreg V-A Spain-Portugal POCTEP 2014–2020’), by Sociedad Española de Neumología y Cirugía Torácica (SEPAR) under project 649/2018, by Sociedad Española de Sueño (SES) under project “Beca de Investigación SES 2019”, and by ‘Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain’ through ‘Instituto de Salud Carlos III’ co-funded with FEDER funds. The Childhood Adenotonsillectomy Trial (CHAT) was supported by National Institutes of Health (NIH) grants HL083075, HL083129, UL1-RR-024134, and UL1 RR024989. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002). F. Vaquerizo-Villar was in receipt of a ‘Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)’ grant from the Ministerio de Educación, Cultura y Deporte (FPU16/02938). D. Álvarez is supported by a "Ramón y Cajal" grant (RYC2019-028566-I) from the 'Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación’ co-funded by the European Social Fund (ESF). V. Barroso-García and E. Santamaría-Vazquez were in a receipt of a ‘Ayuda para financiar la contratación predoctoral de personal investigador’ grant from the Consejería de Educación de la Junta de Castilla y León and the ESF. L. Kheirandish-Gozal and D. Gozal were supported by NIH grants HL130984, HL140548, and AG061824.
    Version del Editor
    https://ieeexplore.ieee.org/abstract/document/9316292
    Propietario de los Derechos
    IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/74125
    Tipo de versión
    info:eu-repo/semantics/acceptedVersion
    Derechos
    restrictedAccess
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    (2)Vaquerizo-Villar-etal_JBHI-2021(accepted_version).PDF
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