End-to-end Automatic Sleep Staging Algorithm using Convolution Neural Network and Bidirectional LSTM

Abstract

In order to measure sleep quality, sleep experts manually classify sleep stages through polysomnography (PSG) signals. However, it is time-consuming and labor-intensive work. Thus, automatic sleep stage classification methods are needed. In this study, we propose an end-to-end automatic sleep staging algorithm using a one-dimensional convolutional neural network (1DCNN) based on an inception network and bidirectional long short-term memory (bi-LSTM). First, a feature map was extracted from input data using the 1D-CNN architecture without preprocessing. Secondly, bi-LSTM learned a stage transition rule using the feature maps. In addition, we used the sleep-EDF public dataset to evaluate our model, and only one channel of EEG signal was used to save computational cost. The accuracy and macro-averaged F1 score of the classification performance were 85.05% and 79.05%, respectively. These results demonstrate state-of-the-art performance compared to previous studies using the same dataset, yielding an effective method for an automatic sleep staging algorithm.