ArticleSleep medicine2022
Aggregating heart rate variability indices across sleep stage epochs ignores significant variance through the night.
Article in Sleep medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Sleep stage classification from ECG using machine learning: Evaluating the impact of signal duration.Neurobiology of sleep and circadian rhythms · 2026Article
- Assessment of Physiological Signals from Photoplethysmography Sensors Compared to an Electrocardiogram Sensor: A Validation Study in Daily Life.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundHeart rate variability (HRV) is a widely utilized biomarker of autonomic regulatory functioning, and concomitant health and pathological states. A growing body of work is exploring HRV under sleeping conditions. Most of this literature utilizes either averaged HRV indices calculated from multiple sleep stage epochs, or averaged HRV throughout the night. Both approaches implicitly assume that HRV within sleep epoch types is consistent throughout the night. Given the robust literature indicating the existence of an endogenous cardiovascular circadian rhythm as well as the potential for effects for cumulative time asleep, we hypothesized that HRV would vary across distinct sleep epochs.
methodsParticipants underwent at least one night of home polysomnography that included electroencephalogram, electromyogram, and electrocardiogram (N = 73). All rapid eye movement (REM) and non-REM stage 2 (N2) sleep epochs with a duration greater than or equal to 5 min were identified for HRV analysis. Time and frequency domain indices of HRV were calculated for each sleep stage epoch. Linear mixed models were used to examine main effects of time on HRV indices for N2 and REM sleeps epochs respectively.
resultsMain effects of time were observed for all models. Patterns emerged for both the N2 and REM epochs, suggesting HRV indices are non-stationary (ie variable) across distinct sleep epochs through the course of the night.
conclusionsThe present findings indicate HRV is non-stationary across sleep stage epochs. Aggregating HRV indices across sleep stage epochs likely obscures important transient effects and increases risk of type-I and type-II errors.
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Registered trials
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