Automated scoring of pre-REM sleep in mice with deep learning

Abstract Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wak...

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Autores principales:	Niklas Grieger, Justus T. C. Schwabedal, Stefanie Wendel, Yvonne Ritze, Stephan Bialonski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d6db134aed5c42e18027c61b042aff8c
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spelling	oai:doaj.org-article:d6db134aed5c42e18027c61b042aff8c2021-12-02T17:52:26ZAutomated scoring of pre-REM sleep in mice with deep learning10.1038/s41598-021-91286-02045-2322https://doaj.org/article/d6db134aed5c42e18027c61b042aff8c2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91286-0https://doaj.org/toc/2045-2322Abstract Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.Niklas GriegerJustus T. C. SchwabedalStefanie WendelYvonne RitzeStephan BialonskiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Niklas Grieger Justus T. C. Schwabedal Stefanie Wendel Yvonne Ritze Stephan Bialonski Automated scoring of pre-REM sleep in mice with deep learning
description	Abstract Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.
format	article
author	Niklas Grieger Justus T. C. Schwabedal Stefanie Wendel Yvonne Ritze Stephan Bialonski
author_facet	Niklas Grieger Justus T. C. Schwabedal Stefanie Wendel Yvonne Ritze Stephan Bialonski
author_sort	Niklas Grieger
title	Automated scoring of pre-REM sleep in mice with deep learning
title_short	Automated scoring of pre-REM sleep in mice with deep learning
title_full	Automated scoring of pre-REM sleep in mice with deep learning
title_fullStr	Automated scoring of pre-REM sleep in mice with deep learning
title_full_unstemmed	Automated scoring of pre-REM sleep in mice with deep learning
title_sort	automated scoring of pre-rem sleep in mice with deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d6db134aed5c42e18027c61b042aff8c
work_keys_str_mv	AT niklasgrieger automatedscoringofpreremsleepinmicewithdeeplearning AT justustcschwabedal automatedscoringofpreremsleepinmicewithdeeplearning AT stefaniewendel automatedscoringofpreremsleepinmicewithdeeplearning AT yvonneritze automatedscoringofpreremsleepinmicewithdeeplearning AT stephanbialonski automatedscoringofpreremsleepinmicewithdeeplearning
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Automated scoring of pre-REM sleep in mice with deep learning

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