School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
School of Computer and Communication Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
Int J Environ Res Public Health. 2022 Apr 25;19(9):5199. doi: 10.3390/ijerph19095199.
Accurate sleep staging results can be used to measure sleep quality, providing a reliable basis for the prevention and diagnosis of sleep-related diseases. The key to sleep staging is the feature representation of EEG signals. Existing approaches rarely consider local features in feature extraction, and fail to distinguish the importance of critical and non-critical local features. We propose an innovative model for automatic sleep staging with single-channel EEG, named CAttSleepNet. We add an attention module to the convolutional neural network (CNN) that can learn the weights of local sequences of EEG signals by exploiting intra-epoch contextual information. Then, a two-layer bidirectional-Long Short-Term Memory (Bi-LSTM) is used to encode the global correlations of successive epochs. Therefore, the feature representations of EEG signals are enhanced by both local and global context correlation. Experimental results achieved on two real-world sleep datasets indicate that the CAttSleepNet model outperforms existing models. Moreover, ablation experiments demonstrate the validity of our proposed attention module.
准确的睡眠分期结果可用于衡量睡眠质量，为睡眠相关疾病的预防和诊断提供可靠依据。睡眠分期的关键在于 EEG 信号的特征表示。现有的方法在特征提取中很少考虑局部特征，也无法区分关键和非关键局部特征的重要性。我们提出了一种基于单通道 EEG 的自动睡眠分期的创新模型，命名为 CAttSleepNet。我们在卷积神经网络（CNN）中添加了一个注意力模块，该模块可以通过利用epoch 内的上下文信息来学习 EEG 信号局部序列的权重。然后，使用两层双向长短期记忆（Bi-LSTM）来编码连续 epoch 的全局相关性。因此，通过局部和全局上下文相关性增强了 EEG 信号的特征表示。在两个真实的睡眠数据集上的实验结果表明，CAttSleepNet 模型优于现有的模型。此外，消融实验证明了我们提出的注意力模块的有效性。
