Improved Deep Learning Classification of Human Sleep Stages

Abstract

We develop a deep convolutional neural network (CNN) that performs sleep stage classification from human sleep EEG and EOG signals. We build and compare several alternative CNN architectures, using nested cross-validation to evaluate performance on unseen patients for different hyperparameter values. Performance after model selection exceeds human expert inter-scorer agreement, even if only a single EEG input channel is used, and is competitive with the state of the art. We further investigate the effect of additional pre-sleep and post-sleep waking data in the training samples. We use t-SNE to visualize the responses of the internal layers of our network, enabling a more complete picture of the development of sleep stage differentiation with layer depth than do activation maximization approaches.