Multimodal Polysomnography-Based Automatic Sleep Stage Classification via Multiview Fusion Network

Sleep staging is a standard diagnostic method for evaluating sleep quality, which would enable early diagnosis of sleep disorders as well as mental diseases. Polysomnography (PSG), a set of physiological signals recorded externally, is a standard media for sleep staging. Developing automatic algorithms to analyze the PSG signals with the purpose of better sleep staging is demanding, as manual assessment is tedious and time-consuming. However, it is challenged by the noisy nature of the PSG signals, i.e., features contributing to sleep staging are embedded in different types and scales of signals in both time and frequency domains. In this article, we propose a hybrid deep learning architecture that uses multimodal PSG signals, specifically electroencephalogram (EEG) and electrooculogram (EOG), and their frequency representations as inputs, to accomplish sleep stage classification tasks. To this end, we design the multiscale local feature extractor (MSLFE) with a multibranch convolutional neural network (CNN) of different convolutional kernel sizes and the global relationship modeling (GRM) module to extract features in both time and frequency domains effectively. A cross-linked fusion (CLF) module is further introduced to enable an effective fusion of multimodal and multiattribute features while avoiding bidirectional representation redundancy for high-quality feature maps. We carried out a set of experiments on the SleepEDF-ST and SleepEDF-SC to validate the effectiveness of the proposed method, where classification performance in terms of precision, recall, and F1-score higher than 84% are obtained on most of the sleep stages. Comparisons with the state-of-the-art methods confirm the effectiveness of the proposed method in improving sleep quality evaluation and diagnosis. Source code is available at: https://github.com/ZJUT-CBS/MMNet.