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.
Highlights
The autonomic nervous system (ANS) plays an important role in regulating sleep cycles [1–4]
We developed three distinct XGBoost classifiers to evaluate the performance of the six entropy measures for three different classification tasks, respectively, as shown below: (1) a three-class classification task to differentiate among W, non-rapid eye movement (NREM), and rapid eye movement (REM); (2) a four-class classification task to differentiate among W, light sleep (LS, combined N1 and N2), deep sleep (DS, or N3), and REM; (3) a five-class classification task to differentiate among W, N1, N2, N3, and REM
We aimed to conduct a systematic study on the complexity of heart rate variability (HRV) of different sleep stages and at the same time explore the importance of entropy measures in sleep staging
Summary
The autonomic nervous system (ANS) plays an important role in regulating sleep cycles [1–4]. There are mainly two types of sleep: non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep [5]. NREM can be further divided into three stages, i.e., stage 1, 2, and 3, which are usually named N1, N2, and N3, respectively. The four sleep stages (i.e., N1, N2, N3, and REM) are cycled through multiple times smoothly within a regular night of sleep. The parasympathetic (vagus) nerve has been found to be important in organizing sleep and wakefulness [6]. During NREM sleep, parasympathetic activity predominates, and the heart rate decreases. Sympathetic nerve activity results in heart rate acceleration in REM sleep [1]. Understanding the changes of the ANS in different stages of sleep is essential for accurately assessing an individual’s sleep quality
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