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Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach
BACKGROUND: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current litera...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912004/ https://www.ncbi.nlm.nih.gov/pubmed/31849551 http://dx.doi.org/10.2147/NSS.S220716 |
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author | Khademi, Aria EL-Manzalawy, Yasser Master, Lindsay Buxton, Orfeu M Honavar, Vasant G |
author_facet | Khademi, Aria EL-Manzalawy, Yasser Master, Lindsay Buxton, Orfeu M Honavar, Vasant G |
author_sort | Khademi, Aria |
collection | PubMed |
description | BACKGROUND: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep. PURPOSE: To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters. PARTICIPANTS AND METHODS: We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses. RESULTS: Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant. CONCLUSION: Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders. |
format | Online Article Text |
id | pubmed-6912004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-69120042019-12-17 Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach Khademi, Aria EL-Manzalawy, Yasser Master, Lindsay Buxton, Orfeu M Honavar, Vasant G Nat Sci Sleep Original Research BACKGROUND: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep. PURPOSE: To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters. PARTICIPANTS AND METHODS: We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses. RESULTS: Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant. CONCLUSION: Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders. Dove 2019-12-11 /pmc/articles/PMC6912004/ /pubmed/31849551 http://dx.doi.org/10.2147/NSS.S220716 Text en © 2019 Khademi et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Khademi, Aria EL-Manzalawy, Yasser Master, Lindsay Buxton, Orfeu M Honavar, Vasant G Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach |
title | Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach |
title_full | Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach |
title_fullStr | Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach |
title_full_unstemmed | Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach |
title_short | Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach |
title_sort | personalized sleep parameters estimation from actigraphy: a machine learning approach |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912004/ https://www.ncbi.nlm.nih.gov/pubmed/31849551 http://dx.doi.org/10.2147/NSS.S220716 |
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