High-throughput visual assessment of sleep stages in mice using machine learning.

Document Type

Article

Publication Date

2-14-2022

Publication Title

Sleep

Keywords

JGM, JMG, Animals, Electroencephalography, Electromyography, Machine Learning, Male, Mice, Mice, Inbred C57BL, Sleep, Sleep Stages, Sleep, REM, Wakefulness

JAX Source

Sleep 2022 Feb 14; 45(2):zsab260

Volume

45

Issue

2

ISSN

1550-9109

PMID

34718812

DOI

https://doi.org/10.1093/sleep/zsab260

Grant

Jackson Laboratory Directors Innovation Fund, DA048634

Abstract

STUDY OBJECTIVES: Sleep is an important biological process that is perturbed in numerous diseases, and assessment of its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice.

METHODS: We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods.

RESULTS: Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 ± 0.05 (mean ± SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to noninvasively detect that sleep stage disturbances induced by amphetamine administration.

CONCLUSIONS: We conclude that machine learning-based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable noninvasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

Comments

We thank members of the Kumar Lab for helpful advice and Taneli Helenius for editing. We thank JAX Information Technology team members Edwardo Zaborowski, Shane Sanders, Rich Brey, David McKenzie, and Jason Macklin for infrastructure support.

Please contact the Joan Staats Library for information regarding this document.

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