MFASleepNet: Multi-view fusion attention-based deep neural network for automatic sleep staging

Sleep staging is important for both the assessment of sleep quality and the diagnosis of sleep-related disorders. Although previous studies have attempted to automatically classify sleep stages and achieved high classification performance, most automatic staging algorithms currently use only the time or frequency domain information of the data, resulting in limited feature representation capability. To address the above problems, we propose a multi-view fusion attention-based deep neural network (MFASleepNet) to classify sleep stages using single-channel EEG signals. MFASleepNet consists of a multi-scale CNN, a multi-view fusion attention module, a global external attention module and a conditional random field. Specifically, the multi-scale CNN can extract features from different frequency bands of the signal. Then the multi-view fusion attention module can fuse multiple views from different time-frequency spaces to improve the feature representation capability. Subsequently, the global external attention module first captures the global feature representation of each sample through the global context block, and then captures the common feature representation of all samples through the external attention mechanism, and outputs a preliminary sleep stage result. Finally, the conditional random field is used to learn sleep stage transition rules to improve classification performance. We evaluate the performance of our model on the Sleep-EDF dataset, and MFASleepNet achieves an accuracy of 87.2% and an F1 score of 81.1% on the Fpz-Cz channel. The experimental results show that our MFASleepNet can optimise the performance of sleep staging and achieve better staging performance than state-of-the-art methods.