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Sleep. 2012 Mar 1;35(3):433-42. doi: 10.5665/sleep.1712.

Assessing REM sleep in mice using video data.

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  • 1Kellogg School of Management, Northwestern University, Evanston, IL, USA.



Assessment of sleep and its substages in mice currently requires implantation of chronic electrodes for measurement of electroencephalogram (EEG) and electromyogram (EMG). This is not ideal for high-throughput screening. To address this deficiency, we present a novel method based on digital video analysis. This methodology extends previous approaches that estimate sleep and wakefulness without EEG/EMG in order to now discriminate rapid eye movement (REM) from non-REM (NREM) sleep.


Studies were conducted in 8 male C57BL/6J mice. EEG/EMG were recorded for 24 hours and manually scored in 10-second epochs. Mouse behavior was continuously recorded by digital video at 10 frames/second. Six variables were extracted from the video for each 10-second epoch (i.e., intraepoch mean of velocity, aspect ratio, and area of the mouse and intraepoch standard deviation of the same variables) and used as inputs for our model.


We focus on estimating features of REM (i.e., time spent in REM, number of bouts, and median bout length) as well as time spent in NREM and WAKE. We also consider the model's epoch-by-epoch scoring performance relative to several alternative approaches. Our model provides good estimates of these features across the day both when averaged across mice and in individual mice, but the epoch-by-epoch agreement is not as good.


There are subtle changes in the area and shape (i.e., aspect ratio) of the mouse as it transitions from NREM to REM, likely due to the atonia of REM, thus allowing our methodology to discriminate these two states. Although REM is relatively rare, our methodology can detect it and assess the amount of REM sleep.


Mouse sleep; REM; high-throughput phenotyping; inbred mouse strains

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