School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin, China.
Electrical and Computer Engineering Department, Purdue University, West Lafayette, Indiana, United States of America.
PLoS One. 2022 Jun 16;17(6):e0269500. doi: 10.1371/journal.pone.0269500. eCollection 2022.
Sleep staging is the basis of sleep evaluation and a key step in the diagnosis of sleep-related diseases. Despite being useful, the existing sleep staging methods have several disadvantages, such as relying on artificial feature extraction, failing to recognize temporal sequence patterns in the long-term associated data, and reaching the accuracy upper limit of sleep staging. Hence, this paper proposes an automatic Electroencephalogram (EEG) sleep signal staging model, which based on Multi-scale Attention Residual Nets (MAResnet) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed model is based on the residual neural network in deep learning. Compared with the traditional residual learning module, the proposed model additionally uses the improved channel and spatial feature attention units and convolution kernels of different sizes in parallel at the same position. Thus, multiscale feature extraction of the EEG sleep signals and residual learning of the neural networks is performed to avoid network degradation. Finally, BiGRU is used to determine the dependence between the sleep stages and to realize the automatic learning of sleep data staging features and sleep cycle extraction. According to the experiment, the classification accuracy and kappa coefficient of the proposed method on sleep-EDF data set are 84.24% and 0.78, which are respectively 0.24% and 0.21 higher than the traditional residual net. At the same time, this paper also verified the proposed method on UCD and SHHS data sets, and the figure of classification accuracy is 79.34% and 81.6%, respectively. Compared to related existing studies, the recognition accuracy is significantly improved, which validates the effectiveness and generalization performance of the proposed method.
睡眠分期是睡眠评估的基础，也是睡眠相关疾病诊断的关键步骤。尽管现有的睡眠分期方法很有用，但它们也存在一些缺点，例如依赖于人工特征提取、无法识别长期相关数据中的时间序列模式，以及达到睡眠分期的精度上限。因此，本文提出了一种基于多尺度注意力残差网络（MAResnet）和双向门控循环单元（BiGRU）的自动脑电图（EEG）睡眠信号分期模型。所提出的模型基于深度学习中的残差神经网络。与传统的残差学习模块相比，该模型还在同一位置并行使用改进的通道和空间特征注意力单元以及不同大小的卷积核，从而对 EEG 睡眠信号进行多尺度特征提取和神经网络的残差学习，以避免网络退化。最后，BiGRU 用于确定睡眠阶段之间的依赖性，实现睡眠数据分期特征和睡眠周期提取的自动学习。根据实验，所提出的方法在睡眠-EDF 数据集上的分类准确率和 Kappa 系数分别为 84.24%和 0.78，分别比传统的残差网络高 0.24%和 0.21。同时，本文还在 UCD 和 SHHS 数据集上验证了所提出的方法，分类准确率分别为 79.34%和 81.6%。与相关的现有研究相比，识别准确率有了显著提高，验证了所提出方法的有效性和泛化性能。
