ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction
ECG-SMART-NET can outperform the state-of-the-art random forest for OMI prediction and is better suited for this task than the original ResNet-18 architecture.
Authors
- Murat Akçakaya (ORCID: https://orcid.org/0000-0001-5094-1931)
- Salah S. Al‐Zaiti (ORCID: https://orcid.org/0000-0002-6862-0658)
- Clifton W. Callaway (ORCID: https://orcid.org/0000-0002-3309-1573)
- Ervin Sejdić (ORCID: https://orcid.org/0000-0003-4987-8298)
- Rui Qi Ji (ORCID: https://orcid.org/0009-0002-6626-9793)
- Christian Martin‐Gill (ORCID: https://orcid.org/0000-0002-3522-2100)
- Samir Saba (ORCID: https://orcid.org/0000-0003-4669-803X)
- Tanmay Gokhale (ORCID: https://orcid.org/0000-0002-3438-2292)
- Jessica K. Zègre‐Hemsey (ORCID: https://orcid.org/0000-0002-5650-0464)
- Zeineb Bouzid (ORCID: https://orcid.org/0000-0002-1095-4444)
- Nathan T. Riek (ORCID: https://orcid.org/0000-0003-3286-560X)
- Stephanie Helman (ORCID: https://orcid.org/0000-0002-7264-2825)
- Karina Kraevsky-Philips
Institutions
- University of North Carolina at Chapel Hill (US)
- Georgia Institute of Technology (US)
- University of Pittsburgh (US)
- University of Toronto (CA)
- University of Pittsburgh Medical Center (US)
- University of Rochester (US)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1109/tbme.2025.3573581
- Citations
- 9
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 12.49
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Center for Advancing Translational Sciences