Paperdoi:10.1038/s41598-020-79294-y
Efficient embedded sleep wake classification for open-source actigraphy
Scientific Reports 2021;11:345 (published 2021-01-11)
TL;DR
End-to-end lightCNNA CNN classifies sleep/wake from raw wrist triaxial acceleration for on-device / embedded use (~2.7k FLOPs, 1,361 params). Validated LOSO against concurrent home portable PSG in n=81 adults (healthy + sleep-disturbance referrals). Median κ 0.78; high wake specificity relative to classic actigraphy literature cited in the paper.
- Form factor: wrist
Abstract
This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features’ extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen’s kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.
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 · 2
All leaderboards| Leaderboard | Method | Result | Rank |
|---|---|---|---|
| own-pisa-ax3-psg-banfi-2021 accuracySleep/wake detection (actigraphy-style) | DORMI | 0.8932accuracy, higher is better | #1 of 1 |
| own-pisa-ax3-psg-banfi-2021 Cohen's kappaSleep/wake detection (actigraphy-style) | DORMI | 0.78Cohen's kappa, higher is better | #1 of 1 |
2 rows are credited to this paper. Ranks apply within each leaderboard only, as reported by the source.
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Cite this paper
Banfi, T., Valigi, N., di Galante, M., d’Ascanio, P., Ciuti, G., & Faraguna, U. (2021). Efficient embedded sleep wake classification for open-source actigraphy. Scientific Reports 2021, 11, 345. https://doi.org/10.1038/s41598-020-79294-y
Banfi, Tommaso, Nicolò Valigi, Marco di Galante, Paola d’Ascanio, Gastone Ciuti, and Ugo Faraguna. 2021. "Efficient embedded sleep wake classification for open-source actigraphy." Scientific Reports 2021 11: 345. https://doi.org/10.1038/s41598-020-79294-y.
Banfi, T., Valigi, N., di Galante, M., d’Ascanio, P., Ciuti, G. and Faraguna, U. (2021) 'Efficient embedded sleep wake classification for open-source actigraphy', Scientific Reports 2021, 11, p. 345. doi: 10.1038/s41598-020-79294-y.
Banfi, Tommaso, et al. "Efficient embedded sleep wake classification for open-source actigraphy." Scientific Reports 2021, vol. 11, 2021, p. 345. https://doi.org/10.1038/s41598-020-79294-y.
T. Banfi, N. Valigi, M. di Galante, P. d’Ascanio, G. Ciuti, and U. Faraguna, "Efficient embedded sleep wake classification for open-source actigraphy," Scientific Reports 2021, vol. 11, p. 345, 2021, doi: 10.1038/s41598-020-79294-y.
@article{banfi2021efficient,
title = {{Efficient embedded sleep wake classification for open-source actigraphy}},
author = {Banfi, Tommaso and Valigi, Nicolò and di Galante, Marco and d’Ascanio, Paola and Ciuti, Gastone and Faraguna, Ugo},
year = {2021},
journal = {Scientific Reports 2021},
volume = {11},
pages = {345},
doi = {10.1038/s41598-020-79294-y}
}Added to the ChatPPG research hub
