ECG-GraphNet: Advanced arrhythmia classification based on graph convolutional networks
Our novel approach and comprehensive analysis underscore the potential advantages of ECG-GraphNet in clinical diagnosis and monitoring.
Authors
- Myeonghun Lee (ORCID: https://orcid.org/0009-0000-3829-8269)
- Jiwoo Lim (ORCID: https://orcid.org/0009-0008-0165-6390)
- J.H. Kim
Institutions
- Soongsil University (KR)
- Korea University (KR)
- Ulsan National Institute of Science and Technology (KR)
Publication Details
- Journal
- Heart Rhythm O2
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1016/j.hroo.2025.05.012
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Korea Medical Device Development Fund
- Ministry of Trade, Industry and Energy
- Ministry of Food and Drug Safety
- Ministry of Science and ICT, South Korea