Extracting Multi-Scale and Salient Features by MSE Based U-Structure and CBAM for Sleep Staging
Extracting Multi-Scale and Salient Features by MSE Based U-Structure and CBAM for Sleep Staging
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DOI：
10.1109/tnsre.2022.3216111
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发表时间：
2023-01-01
影响因子：
4.9
通讯作者：
Xiao, Hanguang
中科院分区：
文献类型：
--
作者：
Liu, Zhi;Luo, Sixin;Xiao, Hanguang
根据世界卫生组织的数据，世界上越来越多的人患有睡眠障碍。自动睡眠分期对于评估睡眠质量和辅助诊断由睡眠障碍引起的精神和神经障碍至关重要。许多研究人员采用深度学习方法进行睡眠阶段分类，并取得了很高的性能。然而，目前还没有一种有效的方法来从生理信号中建模不同睡眠阶段的显著波的内在特征。并且，隐藏在从一个睡眠阶段到另一个睡眠阶段的信号中的转换规则无法被识别和捕获。此外，数据集中的类不平衡问题不利于建立鲁棒的分类模型。为了解决这些问题，我们构建了一个深度神经网络结合MSE（多尺度提取）基于U-结构和CBAM（卷积块注意力模块）从单通道脑电信号中提取多尺度显著波。利用具有均方误差的U结构卷积网络从原始脑电信号中提取多尺度特征。在此之后，CBAM被用来更多地关注显着变化，然后学习连续睡眠阶段之间的过渡规则。进一步，提出了一种类自适应权重交叉熵损失函数，解决了类不平衡问题。在三个公开数据集上的实验表明，与现有的方法相比，我们的模型大大优于最先进的结果。在三个公共数据集上的总体准确性和宏观F1评分（Sleep-EDF-39：90.3%-86.2，Sleep-EDF-153：89.7%-85.2，SHHS：86.8%-83.5）表明，所提出的模型非常有希望完全取代人类专家进行睡眠分期。
According to the World Health Organization, more and more people in the world are suffering from somnipathy. Automatic sleep staging is critical for assessing sleep quality and assisting in the diagnosis of psychiatric and neurological disorders caused by somnipathy. Many researchers employ deep learning methods for sleep stage classification and have achieved high performance. However, there are still no effective methods to modeling intrinsic characteristics of salient wave in different sleep stages from physiological signals. And transition rules hidden in signals from one to another sleep stage cannot be identified and captured. In addition, class imbalance problem in dataset is not conducive to building a robust classification model. To solve these problems, we construct a deep neural network combining MSE(Multi-Scale Extraction) based U-structure and CBAM (Convolutional Block Attention Module) to extract the multi-scale salient waves from single-channel EEG signals. The U-structured convolutional network with MSE is utilized to extract multi-scale features from raw EEG signals. After that, the CBAM is used to focus more on salient variation and then learn transition rules between successive sleep stages. Further, a class adaptive weight cross entropy loss function is proposed to solve the class imbalance problem. Experiments in three public datasets show that our model greatly outperform the state-of-the-art results compared with existing methods. The overall accuracy and macro F1-score (Sleep-EDF-39: 90.3%-86.2, Sleep-EDF-153: 89.7%-85.2, SHHS: 86.8%-83.5) on three public datasets suggest that the proposed model is very promising to completely take place of human experts for sleep staging.