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. 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.
翻译:动物睡眠评分的劳动密集型手工任务的可靠自动化有助于分析长期睡眠研究。近些年来,基于深层次学习的系统能够从数据中学习最佳特征,提高典型睡眠阶段的觉醒、REM和非REM等典型睡眠阶段的评分。 同时,人们已经认识到,在非REM和REEM之间发现的如REM前(0.5)等过渡阶段的统计可以对睡眠的生理特征产生更多的洞察力,目前正在进行生动的调查。我们提议了一个基于简单神经网络结构的分类系统,该结构将经典阶段和REM前小鼠睡眠期的睡眠评分都分。当被限制在古典阶段时,优化的网络展示了最先进的分类表现,在Sample F1评分为0.95分。 当网络不受限制地显示REM前(0.5)比传统阶段低,结果与其他物种的过渡阶段相比,如大鼠睡眠或人类睡眠期过渡阶段的过渡平台。然而,我们观察到了典型的预测顺序,包括典型的睡眠阶段,因为我们典型的睡眠变异性数据序列显示我们的睡眠阶段。