U-Sleep: resilient high-frequency sleep staging

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 diffi...

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Autores principales:	Mathias Perslev, Sune Darkner, Lykke Kempfner, Miki Nikolic, Poul Jørgen Jennum, Christian Igel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/811421ee6cdd4be085436194bdcb2666
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spelling	oai:doaj.org-article:811421ee6cdd4be085436194bdcb26662021-12-02T14:27:46ZU-Sleep: resilient high-frequency sleep staging10.1038/s41746-021-00440-52398-6352https://doaj.org/article/811421ee6cdd4be085436194bdcb26662021-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00440-5https://doaj.org/toc/2398-6352Abstract 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.Mathias PerslevSune DarknerLykke KempfnerMiki NikolicPoul Jørgen JennumChristian IgelNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Mathias Perslev Sune Darkner Lykke Kempfner Miki Nikolic Poul Jørgen Jennum Christian Igel U-Sleep: resilient high-frequency sleep staging
description	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.
format	article
author	Mathias Perslev Sune Darkner Lykke Kempfner Miki Nikolic Poul Jørgen Jennum Christian Igel
author_facet	Mathias Perslev Sune Darkner Lykke Kempfner Miki Nikolic Poul Jørgen Jennum Christian Igel
author_sort	Mathias Perslev
title	U-Sleep: resilient high-frequency sleep staging
title_short	U-Sleep: resilient high-frequency sleep staging
title_full	U-Sleep: resilient high-frequency sleep staging
title_fullStr	U-Sleep: resilient high-frequency sleep staging
title_full_unstemmed	U-Sleep: resilient high-frequency sleep staging
title_sort	u-sleep: resilient high-frequency sleep staging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/811421ee6cdd4be085436194bdcb2666
work_keys_str_mv	AT mathiasperslev usleepresilienthighfrequencysleepstaging AT sunedarkner usleepresilienthighfrequencysleepstaging AT lykkekempfner usleepresilienthighfrequencysleepstaging AT mikinikolic usleepresilienthighfrequencysleepstaging AT pouljørgenjennum usleepresilienthighfrequencysleepstaging AT christianigel usleepresilienthighfrequencysleepstaging
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U-Sleep: resilient high-frequency sleep staging

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