Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome

J. Víctor Marcos*, Roberto Hornero, Ian T. Nabney, Daniel Álvarez, Gonzalo C. Gutiérrez-Tobal, Félix del Campo

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    The relationship between sleep apnoea–hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea–hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.
    Original languageEnglish
    Pages (from-to)216-224
    Number of pages9
    JournalMedical Engineering and Physics
    Issue number3
    Early online date21 Dec 2015
    Publication statusPublished - Mar 2016

    Bibliographical note

    © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


    • oxygen saturation
    • entropy rate
    • approximate entropy
    • sample entropy
    • kernel entropy
    • density estimation


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