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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/21529

    Título
    Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women
    Autor
    Gutierrez Tobal, Gonzalo CésarAutoridad UVA Orcid
    Álvarez González, DanielAutoridad UVA Orcid
    Gómez Pilar, JavierAutoridad UVA Orcid
    Campo Matias, Félix delAutoridad UVA Orcid
    Hornero Sánchez, RobertoAutoridad UVA Orcid
    Año del Documento
    2015
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Entropy 2015, 17, p. 123-141
    Zusammenfassung
    Heart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.
    Materias (normalizadas)
    sleep apnoea
    ISSN
    ISSN 1099-4300
    Revisión por pares
    SI
    DOI
    10.3390/e17010123
    Patrocinador
    Ministerio de Economía, Industria y Competitividad (TEC2011-22987)
    Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13)
    Version del Editor
    www.mdpi.com/journal/entropy
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/21529
    Derechos
    openAccess
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    • DEP71 - Artículos de revista [358]
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    Ene_2015_Entropy_(OpenAccess).pdf
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    Universidad de Valladolid

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