  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.
