The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
An open-source, high-performance tool for automated sleep staging
Open accessAbstract
The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Welch, P. (1967) · IEEE Transactions on Audio and Electroacoustics · doi:10.1109/tau.1967.1161901 · Cited by 12,277
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143 · cited 1×
“The latter is thought to be the most robust and informative score for classification as it naturally takes into account imbalance between sleep stages ( Chicco and Jurman, 2020 ).”
BackgroundBerry, R. B., Budhiraja, R., Gottlieb, D. J., Gozal, D., Iber, C., Kapur, V. K., Marcus, C. L., Mehra, R., Parthasarathy, S., Quan, S. F., et al. (2012) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.2172 · Cited by 5,302
Permutation Entropy: A Natural Complexity Measure for Time Series
Bandt, C., & Pompe, B. (2002) · Physical Review Letters · doi:10.1103/physrevlett.88.174102 · Cited by 4,867
Ohayon, M. M., Carskadon, M. A., Guilleminault, C., & Vitiello, M. V. (2004) · Sleep · doi:10.1093/sleep/27.7.1255 · Cited by 3,349
Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies
Cappuccio, F. P., D'Elia, L., Strazzullo, P., & Miller, M. A. (2010) · Sleep · doi:10.1093/sleep/33.5.585 · Cited by 2,224
Approach to an irregular time series on the basis of the fractal theory
Higuchi, T. (1988) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(88)90081-4 · Cited by 2,160
Cited by
Pupil size reveals arousal level fluctuations in human sleep
Carro-Domínguez, M., Huwiler, S., Oberlin, S., Oesch, T. L., Badii, G., Lüthi, A., Wenderoth, N., Meissner, S. N., & Lustenberger, C. (2025) · Nature Communications · doi:10.1038/s41467-025-57289-5 · Cited by 41
Nicolas, J., King, B. R., Levesque, D., Lazzouni, L., Coffey, E., Swinnen, S., Doyon, J., Carrier, J., & Albouy, G. (2022) · eLife · doi:10.7554/elife.73930 · Cited by 28
Metadata
- Authors
- Vallat, R., & Walker, M. P.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2021-10-14
- Volume
- 10
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W3206812283
- Cited by
- 363
- References
- 53
- FWCI
- 21.23
- Type
- journal article
- Language
- en
- Open access
- gold
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An open-source, high-performance tool for automated sleep staging. https://app.cochat.ai/papers/10.7554/elife.70092
- The paper
- Vallat, R., & Walker, M. P. (2021). An open-source, high-performance tool for automated sleep staging. eLife. https://doi.org/10.7554/elife.70092
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.
