Knowledge graph analysis and visualization of artificial intelligence applied in electrocardiogram

Mengting Yang; Hongchao Zhang; Weichao Liu; Kangle Yong; Jie Xu; Yamei Luo; Henggui Zhang

doi:10.3389/fphys.2023.1118360

Knowledge graph analysis and visualization of artificial intelligence applied in electrocardiogram

Front Physiol. 2023 Feb 9:14:1118360. doi: 10.3389/fphys.2023.1118360. eCollection 2023.

Authors

Mengting Yang^{1

2

3}, Hongchao Zhang⁴, Weichao Liu¹, Kangle Yong², Jie Xu², Yamei Luo², Henggui Zhang^{1

5}

Affiliations

¹ Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Collaborative Innovation Center for Prevention of Cardiovascular Diseases, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China.
² School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
³ College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
⁴ School of Physical Education, Southwest Medical University, Luzhou, China.
⁵ Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom.

Abstract

Background: Electrocardiogram (ECG) provides a straightforward and non-invasive approach for various applications, such as disease classification, biometric identification, emotion recognition, and so on. In recent years, artificial intelligence (AI) shows excellent performance and plays an increasingly important role in electrocardiogram research as well. Objective: This study mainly adopts the literature on the applications of artificial intelligence in electrocardiogram research to focus on the development process through bibliometric and visual knowledge graph methods. Methods: The 2,229 publications collected from the Web of Science Core Collection (WoSCC) database until 2021 are employed as the research objects, and a comprehensive metrology and visualization analysis based on CiteSpace (version 6.1. R3) and VOSviewer (version 1.6.18) platform, which were conducted to explore the co-authorship, co-occurrence and co-citation of countries/regions, institutions, authors, journals, categories, references and keywords regarding artificial intelligence applied in electrocardiogram. Results: In the recent 4 years, both the annual publications and citations of artificial intelligence in electrocardiogram sharply increased. China published the most articles while Singapore had the highest ACP (average citations per article). The most productive institution and authors were Ngee Ann Polytech from Singapore and Acharya U. Rajendra from the University of Technology Sydney. The journal Computers in Biology and Medicine published the most influential publications, and the subject with the most published articles are distributed in Engineering Electrical Electronic. The evolution of research hotspots was analyzed by co-citation references' cluster knowledge visualization domain map. In addition, deep learning, attention mechanism, data augmentation, and so on were the focuses of recent research through the co-occurrence of keywords.

Keywords: CiteSpace; VOSviewer; artificial intelligence (AI); electrocardiogram (ECG); knowledge graph analysis.

Grants and funding

This work was supported by the Youth Project Fund of Southwest Medical University No. 2021ZKQN111, the scientific research project of Sichuan Health Information Society No. 2022014, the Sichuan Science and Technology Program No. 2021YFS0089 and Natural Science Program of Southwest Medical University No. 2020ZRZD008.