Key result
HRV accurately identifies slow wave sleep in ~87% of selected segments for reproducible heart rate assessment.
Why the study?
Does sleep stage affect the reproducibility of heart rate variability parameters in healthy young males?
Observational (n=15)
No
Does sleep stage affect the reproducibility of heart rate variability parameters in healthy young males?
Slow wave sleep provides a highly reproducible state for assessing heart rate and high-frequency power, and can be accurately detected using R-R intervals without full polysomnography.
May support simplified HRV assessment during slow wave sleep; leaves open validation in clinical populations.
Objective: Measurements of heart rate variability (HRV) during sleep are becoming increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep). Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed for the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive RR-intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm. Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS. Conclusions: SWS, a stable state that in contrast to waking is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on RR-intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.
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Herzig et al. (2018) conducted an observational in Healthy (n=15). Heart rate variability (HRV) monitoring during sleep vs. Polysomnography (PSG) sleep staging was evaluated on Accuracy of HRV-based algorithm to identify slow wave sleep (SWS). Slow wave sleep provides a highly reproducible state for determining heart rate and high-frequency power, and 87% of segments selected based on heart rate variability were accurately located within slow wave sleep.
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