Affiliations: [a] College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China | [b] School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China | [c] College of Medical Informatics, Chongqing Medical University, Chongqing, China | [d] College of Bioengineering, Chongqing University, Chongqing, China
Correspondence:
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Corresponding authors: Dechun Zhao, College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China. E-mail: zhaodc@cqupt.edu.cn. Xing Wang, College of Bioengineering, Chongqing University, Chongqing, China. E-mail: wangxing@cqu.edu.cn.
Abstract: BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: 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. RESULTS: 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%. CONCLUSION: 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.
Keywords: Sleep staging, deep learning, one-dimensional convolutional neural network, long short-term memory
