Time-Frequency Convolutional Neural Network for Automatic Sleep Stage Classification Based on Single-Channel EEG

Abstract

In this paper, we propose a new automatic sleep stage classification method based on convolutional neural networks (CNN) using single-channel electroencephalogram (EEG). The sleep stages usually consist of five stages: awake, rapid eye movement (REM), and three non-rapid eye movement stages (N1/N2/SWS). In this method, we introduced two novel ways to convert EEG time series to meaningful matrices which CNN can handle with: the dynamical time-frequency spectrum based on Hilbert-Huang transform and the temporal feature matrix, capturing the characteristics of different sleep stages. Thirtynine whole-night sleep EEG signals from Physionet database were used to evaluate the performance of our proposed method. The results show that our proposed method give good classifications for most sleep stages, especially for awake and the SWS stages. Moreover, we obtain an average accuracy of 84.5%, which outperforms other four existing methods. Different from traditional methods based on artificial feature extracting, this method are simple and more applicable to various time series.