| Title: | The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes |
| Journal: | Proceedings of the National Academy of Sciences of the United States of America |
| Published: | 18 Mar 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35302893/ |
| DOI: | https://doi.org/10.1073/pnas.2116729119 |
| Citations: | 32 (29 in last 2 years) as of 8 Aug 2024 |
Publication 7172
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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.5 Authors
- Machiko Katori
- Shoi Shi
- Koji L. Ode
- Yasuhiro Tomita
- Hiroki R. Ueda
1 Application
| Application ID | Title |
|---|---|
| 48357 | Machine learning based quantitative analysis of human sleep |
Enabling scientific discoveries that improve human health