  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.
