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Sleep classification from wrist-worn accelerometer data using random forests

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 acce...

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Autores principales: Sundararajan, Kalaivani, Georgievska, Sonja, te Lindert, Bart H. W., Gehrman, Philip R., Ramautar, Jennifer, Mazzotti, Diego R., Sabia, Séverine, Weedon, Michael N., van Someren, Eus J. W., Ridder, Lars, Wang, Jian, van Hees, Vincent T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794504/
https://www.ncbi.nlm.nih.gov/pubmed/33420133
http://dx.doi.org/10.1038/s41598-020-79217-x
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author Sundararajan, Kalaivani
Georgievska, Sonja
te Lindert, Bart H. W.
Gehrman, Philip R.
Ramautar, Jennifer
Mazzotti, Diego R.
Sabia, Séverine
Weedon, Michael N.
van Someren, Eus J. W.
Ridder, Lars
Wang, Jian
van Hees, Vincent T.
author_facet Sundararajan, Kalaivani
Georgievska, Sonja
te Lindert, Bart H. W.
Gehrman, Philip R.
Ramautar, Jennifer
Mazzotti, Diego R.
Sabia, Séverine
Weedon, Michael N.
van Someren, Eus J. W.
Ridder, Lars
Wang, Jian
van Hees, Vincent T.
author_sort Sundararajan, Kalaivani
collection PubMed
description 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 ([Formula: see text] ), 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 ([Formula: see text] ). 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.
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spelling pubmed-77945042021-01-12 Sleep classification from wrist-worn accelerometer data using random forests Sundararajan, Kalaivani Georgievska, Sonja te Lindert, Bart H. W. Gehrman, Philip R. Ramautar, Jennifer Mazzotti, Diego R. Sabia, Séverine Weedon, Michael N. van Someren, Eus J. W. Ridder, Lars Wang, Jian van Hees, Vincent T. Sci Rep Article 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 ([Formula: see text] ), 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 ([Formula: see text] ). 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. Nature Publishing Group UK 2021-01-08 /pmc/articles/PMC7794504/ /pubmed/33420133 http://dx.doi.org/10.1038/s41598-020-79217-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Sundararajan, Kalaivani
Georgievska, Sonja
te Lindert, Bart H. W.
Gehrman, Philip R.
Ramautar, Jennifer
Mazzotti, Diego R.
Sabia, Séverine
Weedon, Michael N.
van Someren, Eus J. W.
Ridder, Lars
Wang, Jian
van Hees, Vincent T.
Sleep classification from wrist-worn accelerometer data using random forests
title Sleep classification from wrist-worn accelerometer data using random forests
title_full Sleep classification from wrist-worn accelerometer data using random forests
title_fullStr Sleep classification from wrist-worn accelerometer data using random forests
title_full_unstemmed Sleep classification from wrist-worn accelerometer data using random forests
title_short Sleep classification from wrist-worn accelerometer data using random forests
title_sort sleep classification from wrist-worn accelerometer data using random forests
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794504/
https://www.ncbi.nlm.nih.gov/pubmed/33420133
http://dx.doi.org/10.1038/s41598-020-79217-x
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