Multiscale entropy based analysis of HRV during sleep

Xiao, M.; Yan, Hong; Yang, Xiaoxia; Li, Yanjun; Zhu, Ruiyun

doi:10.1109/bmei.2012.6512919

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…It has been widely used in analyzing cardiac dynamics [18,39,48,49]. Although some studies have conducted comparisons of entropy indices of RR interval time series in certain sleep stages [24,25], it is not well-studied how they change throughout a whole night's sleep, which may be helpful for sleep staging using ECG signals. In this paper, six entropy measures, specifically, ApEn, SampEn, FuzzyEn, DistEn, CE, and PermEn, were computed for RR interval time series based on sliding windows of 270-s or 300-s length.…”

Section: Discussionmentioning

confidence: 99%

“…In prior studies, the SampEn of HRV in the REM stage was shown to be similar to that during wakefulness, whereas it was increased during deep sleep [24]. Based on a multiscale analysis framework using entropy, it was shown that the complexity of HRV during wakefulness and REM was lower than that in NREM [25]. In another study, ApEn decreased from W to NREM and decreased further in REM, whereas SampEn decreased from W to N2, then increased at N3 and decreased again in REM [26].…”

Section: Introductionmentioning

confidence: 99%

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Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Yan

Yang

et al. 2022

Entropy

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Yan

Yang

et al. 2022

Entropy

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Assessing the severity of sleep apnea syndrome based on ballistocardiogram

et al. 2017

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BackgroundSleep Apnea Syndrome (SAS) is a common sleep-related breathing disorder, which affects about 4-7% males and 2-4% females all around the world. Different approaches have been adopted to diagnose SAS and measure its severity, including the gold standard Polysomnography (PSG) in sleep study field as well as several alternative techniques such as single-channel ECG, pulse oximeter and so on. However, many shortcomings still limit their generalization in home environment. In this study, we aim to propose an efficient approach to automatically assess the severity of sleep apnea syndrome based on the ballistocardiogram (BCG) signal, which is non-intrusive and suitable for in home environment.MethodsWe develop an unobtrusive sleep monitoring system to capture the BCG signals, based on which we put forward a three-stage sleep apnea syndrome severity assessment framework, i.e., data preprocessing, sleep-related breathing events (SBEs) detection, and sleep apnea syndrome severity evaluation. First, in the data preprocessing stage, to overcome the limits of BCG signals (e.g., low precision and reliability), we utilize wavelet decomposition to obtain the outline information of heartbeats, and apply a RR correction algorithm to handle missing or spurious RR intervals. Afterwards, in the event detection stage, we propose an automatic sleep-related breathing event detection algorithm named Physio_ICSS based on the iterative cumulative sums of squares (i.e., the ICSS algorithm), which is originally used to detect structural breakpoints in a time series. In particular, to efficiently detect sleep-related breathing events in the obtained time series of RR intervals, the proposed algorithm not only explores the practical factors of sleep-related breathing events (e.g., the limit of lasting duration and possible occurrence sleep stages) but also overcomes the event segmentation issue (e.g., equal-length segmentation method might divide one sleep-related breathing event into different fragments and lead to incorrect results) of existing approaches. Finally, by fusing features extracted from multiple domains, we can identify sleep-related breathing events and assess the severity level of sleep apnea syndrome effectively.ConclusionsExperimental results on 136 individuals of different sleep apnea syndrome severities validate the effectiveness of the proposed framework, with the accuracy of 94.12% (128/136).

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Multiscale entropy based analysis of HRV during sleep

Cited by 2 publications

References 23 publications

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Assessing the severity of sleep apnea syndrome based on ballistocardiogram

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