Detecting sleep outside the clinic using wearable heart rate devices

The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm t...

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Bibliographic Details
Published in:Scientific reports Vol. 12; no. 1; pp. 7956 - 13
Main Authors: Perez-Pozuelo, Ignacio, Posa, Marius, Spathis, Dimitris, Westgate, Kate, Wareham, Nicholas, Mascolo, Cecilia, Brage, Søren, Palotti, Joao
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 13.05.2022
Nature Publishing Group
Nature Portfolio
Subjects:
692/308
692/53
692/700/139
692/700/459
692/700/784
Algorithms
Heart Rate
Humanities and Social Sciences
Humans
Living conditions
multidisciplinary
Polysomnography - methods
Reproducibility of Results
Science
Science (multidisciplinary)
Sleep
Sleep - physiology
Wearable Electronic Devices
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04–0.06 and a total sleep time (TST) deviation of - 2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between - 29.07 and - 55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-11792-7