ECG-XPLAIM: eXPlainable Locally-adaptive Artificial Intelligence Model for arrhythmia detection from large-scale electrocardiogram data
ECG-XPLAIM combines high diagnostic performance with interpretability, addressing a key limitation in AI-driven ECG analysis. The open-source release of ECG-XPLAIM's architecture and pre-trained weights encourages broader adoption, external validation, and further refinement for diverse clinical applications.
Authors
- George Ε. Zakynthinos (ORCID: https://orcid.org/0000-0003-4444-0967)
- Alexios S. Antonopoulos (ORCID: https://orcid.org/0000-0002-5151-0444)
- Theodore G. Papaioannou (ORCID: https://orcid.org/0000-0002-1552-3168)
- Μichael Spartalis (ORCID: https://orcid.org/0000-0002-7442-838X)
- José Millet (ORCID: https://orcid.org/0000-0002-8879-003X)
- Gerasimos Siasos (ORCID: https://orcid.org/0000-0001-7601-8870)
- Julia E. Vogt (ORCID: https://orcid.org/0000-0002-6004-7770)
- Panteleimon Pantelidis (ORCID: https://orcid.org/0000-0001-5394-832X)
- Polychronis Dilaveris (ORCID: https://orcid.org/0000-0003-0399-4111)
- Samuel Ruipérez-Campillo (ORCID: https://orcid.org/0000-0002-5425-4175)
- Ioannis Gialamas (ORCID: https://orcid.org/0009-0003-1700-8244)
- Evangelos K. Oikonomou (ORCID: https://orcid.org/0000-0001-8079-0599)
Institutions
- National and Kapodistrian University of Athens (GR)
- ETH Zurich (CH)
- Universitat Politècnica de València (ES)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-10-16
- DOI
- https://doi.org/10.3389/fcvm.2025.1659971
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Hellenic Society of Cardiology