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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/81493

    Título
    Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis
    Autor
    Barroso-García, Verónica
    Gutiérrez-Tobal, Gonzalo C.
    Kheirandish-Gozal, Leila
    Álvarez, Daniel
    Vaquerizo-Villar, Fernando
    Núñez, Pablo
    del Campo, Félix
    Gozal, David
    Hornero, Roberto
    Año del Documento
    2020-01
    Editorial
    Elsevier
    Descripción
    Producción Científica
    Documento Fuente
    Computer Methods and Programs in Biomedicine, Enero 2020, vol. 183, p. 105083
    Resumen
    Background and objective: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods: 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results: 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. Conclusions: RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.
    Materias Unesco
    1203.04 Inteligencia Artificial
    3325.82 Procesado de señal
    3314 Tecnología Médica
    Palabras Clave
    Airflow (AF)
    Children
    Recurrence plots (RP)
    Sleep Apnoea-Hypopnoea Syndrome (SAHS)
    ISSN
    0169-2607
    Revisión por pares
    SI
    DOI
    10.1016/j.cmpb.2019.105083
    Version del Editor
    https://www.sciencedirect.com/science/article/pii/S0169260719303980?via%3Dihub
    Propietario de los Derechos
    © 2019 Elsevier B.V. All rights reserved
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/81493
    Tipo de versión
    info:eu-repo/semantics/draft
    Derechos
    restrictedAccess
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    CMPB_Barroso-Garcia_2018_R1_UVaDoc.pdf
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    Universidad de Valladolid

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