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

Abstract

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 OPEN ACCESSEntropy 2015, 17 124 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.

Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing

“…Some studies have suggested that certain nonlinear measures of HRV are better predictors of future adverse events in various patient groups than the standard linear measures [13]. For example, Gutiérrez-Tobal et al proved the usefulness of the spectral entropy and multiscale entropy analyses of HRV to detect OSA [6], and Pan et al showed that multiscale entropy analysis may serve as a preliminary screening tool for assessing OSA severity prior to polysomnography [15]. Al-Angari et al applied sample entropy to the analysis of HRV complexity in subjects with and without OSA [14].…”

“…HRV analysis is based on the inter-beat interval (RRI) variation calculated from Electrocardiography (ECG), whereas PRV analysis is based on the pulse-to-pulse interval (PPI) variation calculated from PPG [10]. Some studies have reported a chaotic heart beat behaviour [11,12] suggesting that measures describing nonlinear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by conventional measures [6,10,13]. Several studies have proposed the use of entropy methods, i.e., sample entropy [14] and multiscale entropy [6,15], as measures of complexity to differentiate between the HRV pattern of normal and OSA subjects.…”

“…Thus, analysis of heart rate variability (HRV) has been investigated as a possible predictor of OSA [5][6][7]. Studies have shown that OSA affects the normal variation of heart rate and that both HRV and pulse rate variability (PRV), a surrogate measure of HRV estimated from the photoplethysmogram (PPG) [8], may assist with the identification of OSA events [9,10].…”

“…Several studies have proposed the use of entropy methods, i.e., sample entropy [14] and multiscale entropy [6,15], as measures of complexity to differentiate between the HRV pattern of normal and OSA subjects. However, very few studies have analyzed the heart's nonlinear behavior in the spectral domain [6].…”

Pulse Rate Variability Analysis to Enhance Oximetry as at-Home Alternative for Sleep Apnea Diagnosing

“…Moreover, SAHS is known to change the normal profile of the cardiac signals by the recurrence of bradycardia/tachycardia patterns [4]. Particularly, a recent work from our group reported that spectral entropy (SE) from VLF (0-0.04 Hz), LF (0.04-0.15 Hz), and HF (0.15-0.40 Hz) bands of the heart rate variability signal (HRV) provides more useful information than the corresponding power-based traditional features [9]. This kind of information is not available when using SpO 2 signal alone.…”