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

TL;DR

This paper presents a deep learning system for automated scoring of sleep stages in mice, including transitional pre-REM sleep, achieving high accuracy for classical stages and providing insights into the challenges of classifying transitional states.

Contribution

It introduces a neural network model that scores classical and pre-REM sleep stages in mice, with state-of-the-art accuracy for classical stages and novel insights into transitional sleep classification.

Findings

Achieved 0.95 F1 score for classical sleep stages in mice.

Lower F1 score (0.5) for pre-REM sleep, highlighting classification challenges.

Predicted sleep sequences reflect known sleep dynamics, from Non-REM to REM.

Abstract

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.