Sleep Staging Based on Serialized Dual Attention Network

• URL: http://arxiv.org/abs/2107.08442v1
• Date: Sun, 18 Jul 2021 13:18:12 GMT
• Title: Sleep Staging Based on Serialized Dual Attention Network
• Authors: Huafeng Wang (1), Chonggang Lu (1), Qi Zhang (1), Zhimin Hu (1), Xiaodong Yuan (2), Pingshu Zhang (2), Wanquan Liu (3) ((1) School of Information, North China University of Technology,(2) Department of Neurology, Kailuan General Hospital, Tangshan,(3) School of Intelligent Systems Engineering, Sun Yat-sen University)
• Abstract summary: We propose a deep learning model SDAN based on raw EEG. It serially combines the channel attention and spatial attention mechanisms to filter and highlight key information. It achieves excellent results in the N1 sleep stage compared to other methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sleep staging assumes an important role in the diagnosis of sleep disorders. In general, experts classify sleep stages manually based on polysomnography (PSG), which is quite time-consuming. Meanwhile, the acquisition of multiple signals is complex, which can affect the subject's sleep. Therefore, the use of single-channel electroencephalogram (EEG) for automatic sleep staging has become mainstream. In the literature, a large number of sleep staging methods based on single-channel EEG have been proposed with good results and realize the preliminary automation of sleep staging. However, the performance for most of these methods in the N1 stage is generally not high. In this paper, we propose a deep learning model SDAN based on raw EEG. The method utilises a one-dimensional convolutional neural network (CNN) to automatically extract features from raw EEG. It serially combines the channel attention and spatial attention mechanisms to filter and highlight key information and then uses soft threshold to eliminate redundant information. Additionally, we introduce a residual network to avoid degradation problems caused by network deepening. Experiments were conducted using two datasets with 5-fold cross-validation and hold-out validation method. The final average accuracy, overall accuracy, macro F1 score and Cohen's Kappa coefficient of the model reach 96.74%, 91.86%, 82.64% and 0.8742 on the Sleep-EDF dataset, and 95.98%, 89.96%, 79.08% and 0.8216 on the Sleep-EDFx dataset. Significantly, our model performed superiorly in the N1 stage, with F1 scores of 54.08% and 52.49% on the two datasets respectively. The results show the superiority of our network over the best existing methods, reaching a new state-of-the-art. In particular, the present method achieves excellent results in the N1 sleep stage compared to other methods.

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