Knowledge graph analysis and visualization of artificial intelligence applied in electrocardiogram

• DOI: 10.3389/fphys.2023.1118360
• Publication Date: 2023-02-09T07:21:14Z
• Authors: Mengting Yang; Hongchao Zhang; Weichao Liu; Kangle Yong; Jie Xu; Yamei Luo; Henggui Zhang
• Keywords: Identification; Web of science

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