Abnormal classification of electrocardiogram scatter plot based on token selection vision transformer
This study establishes a vision transformer model with token selection, providing an effective and precise solution for the automated classification of ECG scatter plots. By overcoming the limitations of conventional CNN methods, this model demonstrates exceptional capability in capturing both global and key local features, thus offering a novel approach for the advancement of automated arrhythmia diagnosis technology.
Authors
- Jing Cheng (ORCID: https://orcid.org/0009-0009-5678-6691)
- Changyuan Yao
- Fei He (ORCID: https://orcid.org/0009-0009-0561-9260)
- Qi Cheng (ORCID: https://orcid.org/0000-0002-2123-9139)
- Xingrui Gong (ORCID: https://orcid.org/0000-0002-8180-0661)
- Yingting Wu
- Xinyi Xu (ORCID: https://orcid.org/0009-0002-7459-350X)
Institutions
- Anhui Medical University (CN)
- Anhui University of Traditional Chinese Medicine (CN)
- Second Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251395553
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00