Enhancement of Stress ECG Performance with Machine Learning
This study presents a proof of concept demonstrating that an AI-based model for stress ECG interpretation is feasible and shows acceptable performance.
Authors
- Shruti Krishna Iyengar (ORCID: https://orcid.org/0009-0007-5718-4874)
- Poojan Prajapati (ORCID: https://orcid.org/0009-0005-4959-1344)
- Riya Sudhakar Salian (ORCID: https://orcid.org/0009-0006-4517-046X)
- Komandoor S. Srivathsan (ORCID: https://orcid.org/0000-0001-9732-0243)
- Anil Sriramoju (ORCID: https://orcid.org/0000-0002-1088-5127)
- Hema Srikanth Vemulapalli (ORCID: https://orcid.org/0009-0001-2942-1031)
- Sandeep K. S. Gupta (ORCID: https://orcid.org/0000-0002-6108-5584)
- Ayan Banerjee (ORCID: https://orcid.org/0000-0001-6529-1644)
- Juan F. Rodriguez‐Riascos (ORCID: https://orcid.org/0000-0002-8271-9128)
- Win K. Shen
- Padmapriya Muthu (ORCID: https://orcid.org/0009-0002-5561-0431)
Institutions
- Mayo Clinic Hospital (US)
- Arizona State University (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-09-12
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102141
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00