Affiliation:
1. The Jackson Laboratory, Bar Harbor, ME, USA
2. Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
Abstract
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.
Funder
Jackson Laboratory Directors Innovation Fund
National Institute of Health
Publisher
Oxford University Press (OUP)
Subject
Physiology (medical),Clinical Neurology
Cited by
13 articles.
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