A novel sleep staging network based on multi-scale dual attention. (April 2022)

• Record Type: Journal Article
• Authors: Wang, Huafeng; Lu, Chonggang; Zhang, Qi; Hu, Zhimin; Yuan, Xiaodong; Zhang, Pingshu; Liu, Wanquan
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Sleep staging -- Single-channel EEG -- Deep learning -- Attention mechanism; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103486
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Sleep is extremely important for protecting people's mental and physical health. Once the sleep disorder occurs, people's lives will be greatly affected. Sleep staging plays 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 process of multiple signals is much complex, which can affect the subject's sleep. Therefore, the use of single-channel electroencephalogram (EEG) for automatic sleep staging has become a popular research topic. For EEG signals, several in-siding salient waveforms used to distinguish sleep stages generally have different scales, and a single-scale convolutional neural network(CNN) cannot fully capture the salient waveforms features. To address this issue, we proposed a multiscale dual attention network (MSDAN) based on raw EEG, which utilizes a 1d CNN to automatically extract features from raw EEG. Experiments were conducted using two datasets with 20-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 96.70%, 91.74%, 0.8231 and 0.8723 on the Sleep-EDF dataset, 96.14%, 90.35%, 0.7945 and 0.8284 on the Sleep-EDFx dataset. The results show the superiority of our network over the existing methods, reaching state-of-the-arts.