Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Abstract

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection

“…The basic nature of complexity-based features that are containing “implicit” information about body responses to the human activity and states [ 46 , 47 ] has benefited AI models to help them to overcome the prediction confusedness (compare Table 4 with Table 5 ). As was expected, the findings in this study prove that complexity analysis is also suitable for eye-movement-based data, as useful as its usage in human heart rate [ 16 ], cerebral hemodynamics [ 17 ], blood pressure [ 18 ], and body movements [ 19 , 48 ] data. Moreover, the experimental procedure that resulted in moderate head movement also confirmed that AI models would be suitable for everyday use in distinguishing computer activities in daily life.…”

“…Meanwhile, complexity analysis [ 11 ] was recently used in certain human biometric data comprising heart rate [ 16 ], cerebral hemodynamics [ 17 ], blood pressure [ 18 ], and infants’ limb movements [ 19 ]. The use of the complexity of these biological data can describe the human states related to their health conditions [ 16 , 17 , 18 ] and activities [ 19 , 20 ]. The benefit is the potential application to eye-movement features.…”

Heart Rate Variability Analysis of Healthy Individuals and Patients with Ischemia and Arrhythmia

“…Entropy. In a number of research works [21][22][23], the non-linear parameters AppEn and SampEn (Table 2) are used-a commonly used tool for analyzing the regularity/irregularity of time series.…”

Non-Monotonic Complexity of Stochastic Model of the Channel Gating Dynamics

“…A phenomenon of entropy measures is associated with its ability to characterize the rate of creation of valuable information in a dynamical system, identifying the level of uncertainty or the possibility of an indirect description of the number of available states, which can have a direct impact on many biological aspects [ 10 ]. The different kinds of entropy measures, in the forms of Shannon, Kolmogorov, approximate, or sample entropy, are involved in the analysis of electrophysiological signals, including cardiac rate variability [ 11 , 12 ], electromyography (EMG) [ 13 ], and electroencephalography (EEG) [ 14 ], to name but a few.…”