ECGI Precision Imaging for Cardiac Arrhythmias. Mapping beyond the epicardium (EPICA+)
Problem to Address: Improving treatment for Cardiac Arrhythmia, which affects more than 10 million patients in Europe, is identified as a critical goal in the Strategic Research Agenda for cardiovascular disease. While ablation is the most effective method, it often necessitates multiple procedures, incurring over €13.5 billion in healthcare costs. A personalized ablation strategy could potentially double success rates. A pivotal factor in this challenge is the inadequate technology for precise mapping of volumetric cardiac electrical activity, which is essential for accurately identifying the arrhythmic source in each patient.
Objective: EPICA+ aims to create a groundbreaking technology for comprehensive volumetric heart electrical activity mapping, covering all regions – atria, ventricles, and intermediate myocardial layers. The project will aim at developing and validating the EPICA+ system, integrating electrocardiographic imaging with AI and mathematical modeling.
Methodology: The EPICA+ system will leverage a new mathematical framework and use a substantial existing database comprising ECGI maps from over 1000 patients and more than 20,000 computer electrophysiological models. Within EPICA+ we will create and use a new database with over 100.000 models for further training and validation, culminating in a clinical trial involving 144 patients.
Team Composition: The EPICA+ team unites a multidisciplinary group of 16 experts — Telecommunication Engineers, Mathematicians, Clinicians, and Biomedical Engineers — ensuring a comprehensive approach that blends technological innovation with clinical relevance.
Expected Impact: EPICA+ is poised to significantly improve the treatment efficacy for cardiac arrhythmias for millions of patients. By providing a more accurate and comprehensive understanding of cardiac electrical activity, this technology aims to increase the success rate of treatments, thereby improving patient outcomes and reducing the financial burden on healthcare systems.
This project is funded by the Spanish Agencia Estatal de Investigación PID2023-149812OB-I00
