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- 저자 : Meiqiao Qin (검색결과 1 건)
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Zhi Liu,Meiqiao Qin,Yunhua Lu,Sixin Luo,Qinhan Zhang 대한의용생체공학회 2023 Biomedical Engineering Letters (BMEL) Vol.13 No.4
Sleep staging is often applied to assess the quality of sleep and also be used to prevent and monitor psychiatric disorderscaused by sleep. However, it remains a challenge to extract the discriminative features of salient waveforms in sleep EEG andenable the network to effectively classify sleep stages by emphasizing these crucial features, thus achieving higher accuracy. In this study, an end-to-end deep learning model based on DenseNet for automatic sleep staging is designed and constructed. In the framework, two convolutional branches are devised to extract the underlying features (Two-Frequency Feature) at variousfrequencies, which are then fused and input into the DenseNet module to extract salient waveform features. After that,the Coordinate Attention mechanism is employed to enhance the localization of salient waveform features by emphasizingthe position of salient waveforms and the spatial relationship across the entire frequency spectrum. Finally, the obtainedfeatures are accessed to the fully connected for sleep staging. The model was validated with a 20-fold cross-validation procedureon two public available datasets, and the overall accuracy, kappa coefficient, and MF1 score reached 92.9%, 78.7, 0.86and 90.0%, 75.8, 0.80 on Sleep-EDF-20 and Sleep-EDFx, respectively. Experimental results show that the proposed modelachieves competitive performance for sleep staging compared with the reported approaches under the identical conditions.
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