Quantifying anatomically-based in-silico electrocardiogram variability for cardiac digital twins.

Human cardiac Cardiac digital twins (CDTs) are digital replicas of patient hearts, designed to match clinical observations precisely. The electro-cardiogram (ECG), as the most common non-invasive electrophysiology (EP) measurement, has been recently successfully employed for calibrating CDT. However, ECG-based calibration methods often fail to account for the inherent uncertainties in clinical data acquisition and CDT anatomical generation workflows. As a result, discrepancies inevitably arise between the actual physical and simulated patient EP and ECG. In this study, we aim to qualitatively and quantitatively analyze the impact of these uncertainties on ECG morphology and diagnostic markers, and therefore to assess the reliability of ECG-based CDT calibration. We analyze residual beat-to-beat variability in ECG recordings obtained from three datasets, including healthy subjects and patients treated for ventricular tachycardia and atrial fibrillation. Using a biophysically detailed and anatomically accurate computational model of whole-heart EP combined with a detailed torso model calibrated to closely replicate measured ECG signals, we vary anatomical factors (heart location, orientation, size), heterogeneity in electrical conductivities in the heart and torso, and electrode placements across ECG leads to assess their qualitative impact on ECG morphology. Our study demonstrates that diagnostically relevant ECG features and overall morphology remain close to the ground through ECG independently of the investigated uncertainties. This resilience is consistent with the narrow distribution of ECG due to residual beat-to-beat variability observed in both healthy subjects and patients. Overall, our results suggest that observation uncertainties do not impede an accurate calibration of the CDT.