scholarly journals Circadian Variation of Heart Rate Variability Across Sleep Stages

SLEEP ◽  
2013 ◽  
Vol 36 (12) ◽  
pp. 1919-1928 ◽  
Author(s):  
Philippe Boudreau ◽  
Wei-Hsien Yeh ◽  
Guy A. Dumont ◽  
Diane B. Boivin
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Circadian Variation ◽  
Sleep Stages

Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Low Frequency ◽  
Simple Algorithm ◽  
Sleep Stages ◽  
Spectral Indices ◽  
Current Form ◽  
Power Spectral

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

Modeling heart rate variability including the effect of sleep stages

2016 ◽  
Vol 26 (2) ◽  
pp. 023101 ◽  
Author(s):  
Mateusz Soliński ◽  
Jan Gierałtowski ◽  
Jan Żebrowski
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stages

Scaling exponent values as an ordinary function of the ratio of very low frequency to high frequency powers in heart rate variability over various sleep stages

2016 ◽  
Vol 20 (3) ◽  
pp. 975-985 ◽  
Author(s):  
Ren-Jing Huang ◽  
Ching-Hsiang Lai ◽  
Shin-Da Lee ◽  
Wei-Che Wang ◽  
Ling-Hui Tseng ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Low Frequency ◽  
Sleep Stages ◽  
Scaling Exponent ◽  
Very Low Frequency

scholarly journals Heart Rate Variability Assessment by the Entropy Parameters during Sleep

2020 ◽  
Vol 9 (9) ◽  
pp. 468-472
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Eye Movement ◽  
Discrimination Performance ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Mean Value ◽  
Sleep Stages ◽  
The Mean

The nonlinear heart rate variability (HRV) parameter quantifies autonomic nervous system (ANS) activity based on the complexity or irregularity of an HRV dataset. At present, among various entropy-related parameters during sleep, approximate entropy (ApEn) and sample entropy (SampEn) are not as well understood as other entropy parameters such as Shannon entropy (SE) and conditional entropy (CE). Therefore, in this study, we investigated the characteristics of ApEn and SampEn to differentiate a rapid eye movement (REM) and nonrapid eye movement (NREM) for sleep stages. For nonlinear sleep HRV analysis, two target 10-minute, long-term HRV segments were obtained from each REM and NREM for 16 individual subjects. The target HRV segment was analyzed by moving the 2-minute window forward by 2 s, resulting in 240 results of each ApEn and SampEn. The ApEn and SampEn were averaged to obtain the mean value and standard deviation (SD) of all the results. SampEn provides excellent discrimination performance between REM and NREM in terms of the mean and SD (p<0.0001 and p=0.1989, respectively; 95% CI), but ApEn was inferior to SampEn (p=0.1980 and p=0.9931). The results indicate that SampEn, but not ApEn could be used to discriminate REM from NREM and detect various sleep-related incidents.

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