Single Channel Sleep Staging Based on Unsupervised Feature Learning

Abstract

Sleep staging based on electroencephalogram (EEG) signal, as one of the vital bases of study on sleep diagnosis, has been under massive attention. With the spring up of deep learning these years, the idea of combining deep learning structure with automatic sleep staging has been an attractive topic. However, the labeling of sleep stages requires professional knowledge as well as plenty of time, which raise the barrier to evaluate this idea. In this study, the method of unsupervised feature learning based on a mass of unlabeled data and a small number of labeled data was proposed to accomplish sleep staging. The unsupervised feature learning structure was built based on a pair of symmetric convolutional neural networks, with the help of a shallow neural network classifier to classify sleep stages. The results showed that under the condition of the very few labeled data, sleep staging based on unsupervised feature learning can achieve similar accuracy to supervised feature learning, which provides a new direction for the application of deep learning method in dealing with data that is difficult to label or lack of prior knowledge.