Accurate and Efficient Cardiac Digital Twin from surface ECGs: Insights into Identifiability of Ventricular Conduction System

TL;DR

This paper presents a novel, physiologically grounded method for creating accurate and efficient cardiac digital twins from surface ECGs, addressing the challenge of non-uniqueness in activation patterns and improving clinical applicability.

Contribution

It introduces a physiological prior based on Purkinje-muscle junctions and develops a digital twin ensemble for probabilistic cardiac activation inference.

Findings

Distinct activation maps can produce identical ECGs.

The physiological prior improves model calibration.

Ensemble approach enhances probabilistic inference.

Abstract

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). In this work, we address the challenge of matching subject-specific QRS complexes using anatomically accurate, physiologically grounded cardiac digital twins. By fitting the initial conditions of a cardiac propagation model, our non-invasive method predicts activation patterns during sinus rhythm. For the first time, we demonstrate that distinct activation maps can generate identical surface ECGs. To address this non-uniqueness, we introduce a physiological prior based on the distribution of Purkinje-muscle junctions. Additionally, we develop a digital twin ensemble for probabilistic inference of cardiac activation. Our approach marks a significant advancement in the calibration of cardiac digital twins and enhances their credibility for clinical application.