基于多尺度卷积和自注意力机制的觉醒自动检测方法

Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism

临床上,觉醒事件主要由睡眠技师手动标注,该方法耗时,且主观性强。本研究通过构建基于多尺度卷积和自注意力的卷积神经网络,用1 min单通道脑电信号作为模型的输入,实现端到端的觉醒事件自动检测。研究结果表明,相较于基线模型,本文所提出的方法的精确召回曲线下面积和受试者操作特征曲线下面积均提升约7%。此外,单模态和多模态对比结果显示,单通道脑电信号可实现觉醒事件的有效检测,而简单的多种模态拼接不能提升模型的性能。最后,基于本文所提出的模型,本研究在同一数据库上又实现了自动睡眠分期(平均准确率73%),展示了模型较好的扩展性。本研究为实现可靠的便携式睡眠监测提供了解决方案,同时任务迁移的使用也为临床睡眠数据的自动分析开辟了新道路。

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.