Background. Digital twins for cardiac electrophysiology are an enabling technology for precision cardiology. Current forward models are advanced enough to simulate the cardiac electric activity under different pathophysiological conditions and accurately replicate clinical signals like torso electrocardiograms (ECGs). Here, we address the challenge of matching subject-specific QRS complexes using anatomically accurate, physiologically grounded cardiac digital twins.
Methods. By fitting the initial conditions of a cardiac propagation model, based on the eikonal equation, our non-invasive method predicts activation patterns during sinus rhythm. To address a potential non-uniqueness in the reconstruction, we introduce a physiological prior based on the distribution of Purkinje-muscle junctions (PMJs). Additionally, we develop a digital twin ensemble for probabilistic inference of cardiac activation. The methodology is tested on a realistic in silico model during sinus activation.
Results. For the first time, we demonstrated that several distinct activation maps can generate indistinguishable surface ECGs. Our reconstruction, on average, is accurate in reproducing the ground truth, with maximum standard deviation of 20 ms. Corresponding body surface potentials, computed for each activation, showed highest variability in the heart region.
Conclusions. Our approach marks a significant advancement in the calibration of cardiac digital twins and enhances their credibility for clinical application. High-density surface mapping in the heart region, rather than in the whole body surface, may improve reconstruction.