摘要
针对当前自动睡眠分期方法存在的难点问题,提出了一种结合U^(2)-Net和CBAM融合注意力对EEG-ECG双模态信号进行自动睡眠分期的方法。首先,采用MIT-BIH公开数据集中的EEG-ECG信号进行预处理;然后,利用添加了多尺度特征提取模块的U^(2)-Net网络并行提取EEG和ECG中的波形特征;其次,利用CBAM融合注意力对全部特征进行权重分配;最后,使用Softmax激活函数对睡眠时期进行六分类。结果表明:基于U^(2)-Net和CBAM融合注意力模型进行睡眠分期时,使用ECG单模态信号的六分类总体准确率为80.2%,F1分数为75.3%;使用EEG单模态信号的六分类总体准确率为85.8%,F1分数为81.7%;使用EEG-ECG双模态信号的六分类总体准确率为90.4%,F1分数为85.6%。提出的双模态睡眠分期模型是可行有效的,并且为自动睡眠分期提供了一种新的思路。
Aiming at the difficulties present in current automatic sleep staging methods,a method for automatic sleep staging of EEG and ECG dual modal signals by combining U^(2)-Net and CBAM fusion attention is proposed.Firstly,the EEG-ECG signals in the MIT-BIH public dataset used in this paper are preprocessed.Then,the U^(2)-Net network with multi-scale feature extraction module is used to extract waveform features in EEG and ECG in parallel.Secondly,CBAM fusion attention is used to assign weights to all features.Finally,the Softmax activation function is used to classify sleep periods into six.The results show that when sleep staging is performed based on U^(2)-Net and CBAM fusion attention models,the overall accuracy of hexaclassification using ECG single-modal signals is 80.2%,and the F1 score is 75.3%.The overall accuracy of six classifications using EEG single-modal signals was 85.8%,and the F1 score was 81.7%;The overall accuracy of the six classifications using EEG-ECG dual-modal signals was 90.4%,and the F1 score was 85.6%.This shows that the bimodal sleep staging model proposed in this paper is feasible and effective,and provides a new idea for automatic sleep staging.
作者
赵倩
李锦
凤飞龙
强宁
胡静
ZHAO Qian;LI Jin;FENG Feilong;QIANG Ning;HU Jing(School of Physics and Information Technology,Shaanxi Normal University,Xi'an 710119,Shaanxi,China)
出处
《陕西师范大学学报(自然科学版)》
北大核心
2025年第1期1-11,共11页
Journal of Shaanxi Normal University:Natural Science Edition
基金
国家自然科学基金(11974231)。
