College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China.
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China.
Technol Health Care. 2022;30(2):323-336. doi: 10.3233/THC-212847.
Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection.
This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory.
The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result.
The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%.
These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.
睡眠分期是睡眠研究的重要组成部分。传统的基于机器学习的自动睡眠分期需要广泛的特征提取和选择。
本文提出了一种基于一维卷积神经网络和长短时记忆的无需特征提取的深度学习算法。
该算法可以使用脑电图信号自动将睡眠分为清醒期、非快速眼动睡眠期（N1～N3）和快速眼动期 5 个阶段。原始信号经过小波变换处理。然后，将处理后的信号直接输入到深度学习算法中，以获得分期结果。
使用 Fpz-Cz 脑电图信号时，分期的准确率为 93.47%。当使用 Fpz-Cz 和脑电图信号时，该算法可以获得最高的准确率 94.15%。
这些结果表明，该算法适用于不同的生理信号，可以实现端到端的自动睡眠分期，无需任何手动特征提取。
