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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-91286-0 |
| _version_ | 1823972850806030336 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-18T03:47:37Z |
| format | Article |
| id | doaj.art-d6db134aed5c42e18027c61b042aff8c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-18T03:47:37Z |
| publishDate | 2021-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d6db134aed5c42e18027c61b042aff8c2022-12-21T21:22:01ZengNature PortfolioScientific Reports2045-23222021-06-0111111410.1038/s41598-021-91286-0Automated scoring of pre-REM sleep in mice with deep learningNiklas Grieger0Justus T. C. Schwabedal1Stefanie Wendel2Yvonne Ritze3Stephan Bialonski4Department of Medical Engineering and Technomathematics, FH Aachen University of Applied SciencesIndependent researcherDepartment of Medical Psychology and Behavioral Neurobiology, University of TübingenDepartment of Medical Psychology and Behavioral Neurobiology, University of TübingenDepartment of Medical Engineering and Technomathematics, FH Aachen University of Applied SciencesAbstract 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.https://doi.org/10.1038/s41598-021-91286-0 |
| spellingShingle | Niklas Grieger Justus T. C. Schwabedal Stefanie Wendel Yvonne Ritze Stephan Bialonski Automated scoring of pre-REM sleep in mice with deep learning Scientific Reports |
| title | 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_short | Automated scoring of pre-REM sleep in mice with deep learning |
| title_sort | automated scoring of pre rem sleep in mice with deep learning |
| url | https://doi.org/10.1038/s41598-021-91286-0 |
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