Multiscale Entropy Analysis of Heart Rate Variability for Assessing the Severity of Sleep Disordered Breathing

Abstract

Obstructive sleep apnea (OSA) is an independent cardiovascular risk factor to which autonomic nervous dysfunction has been reported to be an important contributor. Ninety subjects recruited from the sleep center of a single medical center were divided into four groups: normal snoring subjects without OSA (apnea hypopnea index, AHI < 5, n = 11), mild OSA (5 ≤ AHI < 15, n = 10), moderate OSA (15 ≤ AHI < 30, n = 24), and severe OSA (AHI ≥ 30, n = 45). Demographic (i.e., age, gender), anthropometric (i.e., body mass index, neck circumference), and polysomnographic (PSG) data were recorded and compared among the different groups. For each subject, R-R intervals (RRI) from 10 segments of 10-minute electrocardiogram recordings during non-rapid eye movement sleep at stage N2 were acquired and analyzed for heart rate variability (HRV) and sample entropy using multiscale entropy index (MEI) that was divided into small scale (MEISS, scale 1-5) and OPEN ACCESSEntropy 2015, 17 232 large scale (MEILS, scale 6-10). Our results not only demonstrated that MEISS could successfully distinguish normal snoring subjects and those with mild OSA from those with moderate and severe disease, but also revealed good correlation between MEISS and AHI with Spearman correlation analysis (r = −0.684, p < 0.001). Therefore, using the two parameters of EEG and ECG, MEISS may serve as a simple preliminary screening tool for assessing the severity of OSA before proceeding to PSG analysis.

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].…”

“…Several studies have shown that OSA affects the normal variation of heart rate and that both HRV [5,7,15,31] and PRV [9,32] might help identifying OSA events. 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].…”

“…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. However, very few studies have analyzed the heart's nonlinear behavior in the spectral domain [6].…”

Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy

“…Our study analyzed trajectories and EMG signals by dividing the multiscale entropy index (MEI) into four small scale intervals using the MSFuzzyEn approach for comparison [30]:…”

Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping

“…Because time-series data of the GM morphology are inherently complex, chaos and nonlinear dynamics analyses of these time-series data are therefore suitable mathematical techniques for extracting their informative statistical properties. Moreover, chaos and nonlinear dynamics have been increasingly reported as effective computational methods for analyzing complex data in medicine and biology [30][31][32][33][34][35][36]. We therefore explore several methods of chaos and nonlinear dynamics to extract features from time-series data of the GM spatial structure of the brain on MRI.…”