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Automatic Sleep/Wake Identification From Wrist Activity
Open accessAbstract
The purpose of this study was to develop and validate automatic scoring methods to distinguish sleep from wakefulness based on wrist activity. Forty-one subjects (18 normals and 23 with sleep or psychiatric disorders) wore a wrist actigraph during overnight polysomnography. In a randomly selected subsample of 20 subjects, candidate sleep/wake prediction algorithms were iteratively optimized against standard sleep/wake scores. The optimal algorithms obtained for various data collection epoch lengths were then prospectively tested on the remaining 21 subjects. The final algorithms correctly distinguished sleep from wakefulness approximately 88% of the time. Actigraphic sleep percentage and sleep latency estimates correlated 0.82 and 0.90, respectively, with corresponding parameters scored from the polysomnogram (p < 0.0001). Automatic scoring of wrist activity provides valuable information about sleep and wakefulness that could be useful in both clinical and research applications.
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Recognition of sleep dependent memory consolidation with multi-modal sensor data
Sano, A., & Picard, R. W. (2013) · 2013 IEEE International Conference on Body Sensor Networks · doi:10.1109/bsn.2013.6575479 · Cited by 12
Tonetti, L., Fábregas, S. E., Fabbri, M., Occhionero, M., Erbacci, A., Martoni, M., & Natale, V. (2013) · Biological Rhythm Research · doi:10.1080/09291016.2012.692252 · Cited by 7
Metadata
- Authors
- Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., & Gillin, J. C.
- Venue
- Sleep
- Publisher
- Oxford University Press (OUP)
- Published
- 1992-09
- Volume
- 15
- Issue
- 5
- Pages
- 461-469
- ISSN
- 0161-8105 (ISSN-L)
- 1550-9109
- OpenAlex
- W117545607
- Cited by
- 1,941
- References
- 0
- FWCI
- 9.21
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.