Paperdoi:10.1038/s41598-020-79217-x
Sleep classification from wrist-worn accelerometer data using random forests
Scientific Reports 2021 (published 2021-01-08)
TL;DR
Random forests classify sleep/wake (and non-wear) from wrist accelerometer during clinic PSG. Test F1 73.93% (n=24); non-wear F1 >93%. Open-sourced models integrable with ggir.
- Form factor: wrist
Abstract
Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ( F1-score > 93.31%
F1-score > 93.31 %
), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ( r=.60
r = . 60
). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0
Results on ChatPPG leaderboards · 1
All leaderboards| Leaderboard | Method | Result | Rank |
|---|---|---|---|
| sleep clinic, 2015 Class F1 for one stated class , not macroSleep/wake detection (actigraphy-style) | Sadeh sleep–wake scoring | 0.7393Class F1 for one stated class , not macro, higher is better | #1 of 1 |
1 row is credited to this paper. Ranks apply within each leaderboard only, as reported by the source.
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Cite this paper
Sundararajan, K., Georgievska, S., Lindert, B. H. W. T., et al. (2021). Sleep classification from wrist-worn accelerometer data using random forests. Scientific Reports 2021. https://doi.org/10.1038/s41598-020-79217-x
Sundararajan, Kalaivani, Sonja Georgievska, and Bart H. W. te Lindert, et al. 2021. "Sleep classification from wrist-worn accelerometer data using random forests." Scientific Reports 2021. https://doi.org/10.1038/s41598-020-79217-x.
Sundararajan, K., Georgievska, S. and Lindert, B. H. W. T. et al. (2021) 'Sleep classification from wrist-worn accelerometer data using random forests', Scientific Reports 2021. doi: 10.1038/s41598-020-79217-x.
Sundararajan, Kalaivani, et al. "Sleep classification from wrist-worn accelerometer data using random forests." Scientific Reports 2021, 2021. https://doi.org/10.1038/s41598-020-79217-x.
K. Sundararajan et al., "Sleep classification from wrist-worn accelerometer data using random forests," Scientific Reports 2021, 2021, doi: 10.1038/s41598-020-79217-x.
@article{sundararajan2021sleep,
title = {{Sleep classification from wrist-worn accelerometer data using random forests}},
author = {Sundararajan, Kalaivani and Georgievska, Sonja and Lindert, Bart H. W. te and others},
year = {2021},
journal = {Scientific Reports 2021},
doi = {10.1038/s41598-020-79217-x}
}Added to the ChatPPG research hub
