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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2699-2704

Single channel EEG signal automatic sleep staging algorithm based on deep reinforcement learning

Zhao Yanjing1a,2
Zhou Qiang1a,2
Liu Xin1a,2
Li Wan1b,2
Tian Yunzhi1a,2
1. a. School of Electrical & Control Engineering, b. School of Electronic Information & Artificial Intelligence, Shaanxi University of Science & Technology, Xi'an 710021, China
2. Shaanxi Artificial Intelligence Joint Laboratory, Xi'an 710021, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0008

Abstract

Currently, human sleep staging methods based on electroencephalogram(EEG) signals show a trend towards single-channel and deep network models, however, single-channel information acquisition makes EEG lose the positional information of brain regions, and the features characterizing sleep stages in EEG tend to be sparse and thus difficult to extract, at the same time, the common problems of deep networks-the artificial setting of the model and its training hyperparameters make the training process blind and inefficient, and these problems lead to the low accuracy of automatic sleep staging methods. Therefore, this paper proposed to use the inter-layer feature reuse function of DenseNet to explore the sleep state information hidden in EEG signals, and improved the DenseNet model for the low-frequency characteristics of single-channel EEG signals in the frequency domain and the long-range dependence of single-channel EEG signals in the time domain, so as to achieve the fast and accurate sleep staging of the human body. In order to further improve the performance of DenseNet, it used a deep deterministic policy gradient(DDPG) algorithm to optimize and automatically adjust the key hyperparameters of DenseNet using the reinforcement learning idea during the network learning and training process. The experimental results show that the staging accuracy of the algorithm model on the Sleep-EDFx dataset reaches 89.23%, and the overall performance is better than other advanced staging algorithms in recent years, demonstrating good application prospects.

Foundation Support

国家自然科学基金资助项目(62101312)
陕西省科技厅工业项目(2024GX-YBXM-544)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0008
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2699-2704
Serial Number: 1001-3695(2024)09-019-2699-06

Publish History

[2024-03-19] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

赵彦晶, 周强, 刘鑫, 等. 基于深度强化学习的单通道EEG信号自动睡眠分期算法 [J]. 计算机应用研究, 2024, 41 (9): 2699-2704. (Zhao Yanjing, Zhou Qiang, Liu Xin, et al. Single channel EEG signal automatic sleep staging algorithm based on deep reinforcement learning [J]. Application Research of Computers, 2024, 41 (9): 2699-2704. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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