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

• Published in: Medical engineering & physics 2016-03, Vol.38 (3), p.216-224
• Main Authors: Marcos, J. Víctor, Hornero, Roberto, Nabney, Ian T, Álvarez, Daniel, Gutiérrez-Tobal, Gonzalo C, del Campo, Félix
• Format: Article
• Language: English
• Subjects: Approximate entropy; Density estimation; Diagnostic systems; Disorders; Entropy; Entropy rate; Female; Humans; Irregularities; Kernel entropy; Male; Middle Aged; Oximetry; Oxygen - metabolism; Oxygen saturation; Radiology; Recording; Regularity; Sample entropy; Signal Processing, Computer-Assisted; Sleep; Sleep Apnea Syndromes - diagnosis; Sleep Apnea Syndromes - metabolism
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2015.11.010

Abstract 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.