The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 2 of 4790
Back to Result List

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

  • 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Niklas Grieger, Justus T. C. Schwabedal, Stefanie Wendel, Yvonne Ritze, Stephan BialonskiORCiD
DOI:https://doi.org/10.1038/s41598-021-91286-0
DOI:https://doi.org/10.21269/9596
ISSN:2045-2322
Parent Title (English):Scientific Reports
Publisher:Springer Nature
Place of publication:London
Document Type:Article
Language:English
Year of Completion:2021
Publishing Institution:Fachhochschule Aachen
Date of the Publication (Server):2021/06/17
Volume:11
Issue:Art. 12245
Note:
Corresponding author: Stephan Bialonski
Link:https://doi.org/10.1038/s41598-021-91286-0
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
collections:Verlag / Springer Nature
Open Access / Gold
Licence (German):License LogoCreative Commons - Namensnennung