The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes. Katori, M., Shi, S., Ode, K. L, Tomita, Y., & Ueda, H. R Proc. Natl. Acad. Sci. U. S. A., 119(12):e2116729119, Proceedings of the National Academy of Sciences, 22 March, 2022. ![The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.
@ARTICLE{Katori2022-ii,
title = "The 103,200-arm acceleration dataset in the {UK} Biobank revealed
a landscape of human sleep phenotypes",
author = "Katori, Machiko and Shi, Shoi and Ode, Koji L and Tomita,
Yasuhiro and Ueda, Hiroki R",
journal = "Proc. Natl. Acad. Sci. U. S. A.",
publisher = "Proceedings of the National Academy of Sciences",
volume = 119,
number = 12,
pages = "e2116729119",
abstract = "SignificanceHuman sleep phenotypes are diversified by genetic and
environmental factors, and a quantitative classification of sleep
phenotypes would lead to the advancement of biomedical mechanisms
underlying human sleep diversity. To achieve that, a pipeline of
data analysis, including a state-of-the-art sleep/wake
classification algorithm, the uniform manifold approximation and
projection (UMAP) dimension reduction method, and the
density-based spatial clustering of applications with noise
(DBSCAN) clustering method, was applied to the 100,000-arm
acceleration dataset. This revealed 16 clusters, including seven
different insomnia-like phenotypes. This kind of quantitative
pipeline of sleep analysis is expected to promote data-based
diagnosis of sleep disorders and psychiatric disorders that tend
to be complicated by sleep disorders.",
month = "22~" # mar,
year = 2022,
url = "http://dx.doi.org/10.1073/pnas.2116729119",
file = "All Papers/K/Katori et al. 2022 - The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes.pdf",
keywords = "UMAP; clustering; insomnia; sleep; sleep landscape;Laboratory
publication;Laboratory publication/Since2021",
language = "en"
}
Downloads: 0
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