BrainSleepNet: Learning Multivariate EEG Representation for Automatic Sleep Staging

Abstract

Sleep is one of the most fundamental physiological activities of human beings. Automatic sleep staging can efficiently assist human experts to diagnose the sleep health of people. However, most of the existing methods only considered one or two kinds of time-domain, frequency-domain, and spatial-domain information from EEG signals. Therefore, how to make full use of the complementarity of different features of EEG signals is still a challenge. To tackle this challenge, in this paper we design BrainSleepNet to capture the comprehensive features of multivariant EEG signals for automatic sleep staging. BrainSleepNet consists of an EEG temporal feature extraction module and an EEG spectral-spatial feature extraction module for the temporal-spectral-spatial representation of EEG signals. To the best of our knowledge, it is the first attempt to integrate EEG temporal-spectral-spatial features simultaneously in a unified model for sleep staging. Experiments on the benchmark dataset MASSSS3 demonstrate that BrainSleepNet outperforms all baseline models. The implementation code of BrainSleepNet is available at https://github.com/ziyujia/sleep.