Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

• Published in: Entropy (Basel, Switzerland) Vol. 17; no. 1; pp. 123 - 141
• Main Authors: Gutiérrez-Tobal, Gonzalo, Álvarez, Daniel, Gomez-Pilar, Javier, del Campo, Félix, Hornero, Roberto
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2015
• Subjects: Dynamics; Entropy; Heart rate; heart rate variability; Men; Modelling; multiscale entropy; Recording; Sleep; sleep apnoea; spectral entropy
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e17010123

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.