Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
This work addresses the need for automation in sleep studies for researchers, potentially facilitating long-term data-driven research in sleep biology, though it is incremental as it applies existing deep learning methods to this specific domain.
The authors tackled the time-consuming task of sleep stage classification in mice by developing a deep neural network that automatically predicts Wake, Non-REM, and REM states from EEG and EMG recordings, achieving excellent scoring results on out-of-sample data with 4-second epoch predictions and artifact detection.
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.