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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1123-1130

ECG-based emotion recognition based on contrastive learning

Long Jinyi1a,1b,2
Fang Jinglong1a
Liu Siwei1a
Wu Hanrui1a
Zhang Jia1a
1. a. College of Information Science & Technology, b. Guangdong Key Lab of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou 510632, China
2. Pazhou Lab, Guangzhou 510335, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0354

Abstract

The majority of current machine learning and deep learning solutions for ECG-based emotion recognition utilize fully-supervised learning methods. Several limitations of this approach are that large human-annotated datasets and computing resources are required. Furthermore, the feature representations learned by fully supervised methods tend to be task-specific with limited generalization capability. In response to these issues, this paper proposed an approach based on contrastive lear-ning for ECG-based emotion recognition, which consisted of two steps, such as pre-training and fine-tuning. The goal of pre-training was to learn representations from unlabeled EGG data through contrastive learning. Specifically, it designed two simple and efficient ECG signal augmentation methods, and used these two views to learn robust temporal representations in the time contrastive module, followed by learning discriminative feature representations in the context contrastive module. Fine-tuning used labelled data to learn emotion recognition. Experiments show that the proposed method has reached the maximum accuracy on three public ECG-based emotion recognition datasets. Additionally, the proposed method shows high efficiency under the semi-supervised settings.

Foundation Support

国家自然科学基金资助项目(62276115)
广东省中医药信息化重点实验室资助项目(2021B1212040007)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0354
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: System Development & Application
Pages: 1123-1130
Serial Number: 1001-3695(2024)04-024-1123-08

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

龙锦益, 方景龙, 刘斯为, 等. 基于对比学习的心电信号情绪识别方法 [J]. 计算机应用研究, 2024, 41 (4): 1123-1130. (Long Jinyi, Fang Jinglong, Liu Siwei, et al. ECG-based emotion recognition based on contrastive learning [J]. Application Research of Computers, 2024, 41 (4): 1123-1130. )

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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