Li Fan, Xu Yan, Zhang Bin, Cong Fengyu
School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.
Department of Psychiatry, Nanfang Hospital, Southern Medical University, Guangzhou 510515, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Feb 25;40(1):27-34. doi: 10.7507/1001-5515.202204052.
In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.
在临床上，由技术人员进行人工评分是睡眠觉醒检测的主要方法。这种方法既耗时又主观。本研究旨在通过构建一个基于多尺度卷积层和自注意力机制的卷积神经网络，并使用1分钟单通道脑电图（EEG）信号作为输入，来实现端到端的睡眠觉醒事件检测。与基线模型的性能相比，所提方法的结果表明，精确率-召回率曲线下的平均面积和受试者工作特征曲线下的面积均提高了7%。此外，我们还比较了单模态和多模态对所提模型性能的影响。结果揭示了单通道EEG信号在自动睡眠觉醒检测中的作用。然而，多模态信号的简单组合可能对模型性能的提升产生反作用。最后，我们还探索了所提模型的可扩展性，并将该模型转移到同一数据集中的自动睡眠分期任务中。73%的平均准确率也表明了所提方法在任务转移方面的作用。本研究为便携式睡眠监测的发展提供了一种潜在的解决方案，并为使用迁移学习方法进行自动睡眠数据分析铺平了道路。
