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Perslev M, Darkner S, Kempfner L, et al. U-Sleep: resilient high-frequency sleep staging. NPJ Digit Med. 2021;4(1):72doi: 10.1038/s41746-021-00440-5.
Perslev, M., Darkner, S., Kempfner, L., Nikolic, M., Jennum, P. J., & Igel, C. (2021). U-Sleep: resilient high-frequency sleep staging. NPJ digital medicine, 4(1), 72. https://doi.org/10.1038/s41746-021-00440-5
Perslev, Mathias, et al. "U-Sleep: resilient high-frequency sleep staging." NPJ digital medicine vol. 4,1 (2021): 72. doi: https://doi.org/10.1038/s41746-021-00440-5
Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C. U-Sleep: resilient high-frequency sleep staging. NPJ Digit Med. 2021 Apr 15;4(1):72. doi: 10.1038/s41746-021-00440-5. PMID: 33859353; PMCID: PMC8050216.
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NPJ Digit Med. 2021 Apr 15;4(1):72. doi: 10.1038/s41746-021-00440-5.

U-Sleep: resilient high-frequency sleep staging.

NPJ digital medicine

Mathias Perslev, Sune Darkner, Lykke Kempfner, Miki Nikolic, Poul Jørgen Jennum, Christian Igel

Affiliations

  1. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
  2. Danish Center for Sleep Medicine, Rigshospitalet, Copenhagen, Denmark.
  3. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark. [email protected].

PMID: 33859353 PMCID: PMC8050216 DOI: 10.1038/s41746-021-00440-5

Abstract

Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

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