System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1137-1142,1167

Automatic detection technology of resting state EEG data for mild cognitive decline based on convolutional neural network

Duan Zijing1a
Zhao Binglei1b
Li Chunbo1b,2
Guo Wei1a
1. a. School of Electronic Information & Electrical Engineering, b. Institute of Psychological & Behavioral Science, Shanghai Jiao Tong University, Shanghai 200240, China
2. Shanghai Mental Health Center, Shanghai Jiao Tong University, Shanghai 200030, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0417

Abstract

Mild cognitive decline is the early stage of Alzheimer's disease, and the feature extraction and classification of mild cognitive decline using EEG signals is an important method for diagnosing mild cognitive decline. In the automatic detection technology of mild cognitive decline based on electroencephalogram artificial intelligence, the existing research only extracted a certain feature in the electroencephalogram signal or simply concatenates multiple features, which caused these methods to fail to well consider the correlation between different features and it would cause the problem of dimensional disaster. This paper proposed a convolutional neural network based automatic detection algorithm for resting state electroencephalogram data of mild cognitive decline. By extracting the power spectrum and brain network features of the electroencephalogram, it fused the two features by matrix operation, and designed a convolutional neural network to classify the fused features. This method achieves a high accuracy rate on the data set collected by a hospital in Shanghai. In addition, by inputting different subsets of the feature set, this method found the few groups of features that contribute the most to mild cognitive decline, thereby it also had a certain interpretability. Experiments on the dataset of this paper, it proves the advantages of the power brain network for the automatic diagnosis of mild cognitive decline.

Foundation Support

上海市自然科学基金资助项目(20ZR1429700)
上海交通大学新进教师启动计划资助项目(20X100040054)
医工交叉(交大之星)青年项目(21X010301629)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0417
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: System Development & Application
Pages: 1137-1142,1167
Serial Number: 1001-3695(2022)04-030-1137-06

Publish History

[2021-12-06] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

段子敬, 赵冰蕾, 李春波, 等. 基于特征融合方法的轻微认知衰退静息态脑电数据自动检测技术研究 [J]. 计算机应用研究, 2022, 39 (4): 1137-1142,1167. (Duan Zijing, Zhao Binglei, Li Chunbo, et al. Automatic detection technology of resting state EEG data for mild cognitive decline based on convolutional neural network [J]. Application Research of Computers, 2022, 39 (4): 1137-1142,1167. )

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.

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