使用深度学习对小鼠的睡眠阶段和EEG伪影进行自动分类
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
摘要：
睡眠评分是睡眠研究中一项必要且耗时的任务。在动物模型（如小鼠）或人类中，将这一繁琐的过程自动化有望促进长期研究，并促进睡眠生物学成为一个数据驱动的领域。我们引入了一个深度神经网络模型，该模型能够从EEG和EMG记录中预测小鼠的不同意识状态（清醒，非REM，REM），并对样本外数据进行了出色的评分。预测是在4秒长度的时期上进行的，并且时期被分类为无伪影或无伪影。模型架构借鉴了深度学习和卷积神经网络研究的最新进展。与以前的自动睡眠评分方法相比，我们的模型不依赖于手动定义的数据特征，而是自动学习预测特征。我们希望像我们这样的深度学习模型能够广泛应用于不同的领域，自动化许多以前难以解决的重复性认知任务。
英文摘要：
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven field. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifact-free or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
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