Inference of Number and Location of Purkinje Root Nodes and Ventricular Conduction Properties from Clinical 12-Lead ECGs for Cardiac Digital Twinning

Julia Camps1, Zhinuo Jenny Wang1, Rafael Sebastian2, Xin Zhou1, Brodie Lawson3, Lucas Berg4, Kevin Burrage5, Vicente Grau1, Rodrigo Weber4, Blanca Rodriguez1
1University of Oxford, 2University of Valencia, 3ARC Centre of Excellence for Mathematical and Statistical Frontiers (Queensland University of Technology), 4Federal University of Juiz de Fora, 5Queensland University of Technology


The Purkinje network plays a determinant role in the electrical activation sequence of the human heart. However, Purkinje properties cannot be clinically measured directly. Recent studies have successfully demonstrated cardiac digital twins without Purkinje networks, using inference methods integrating cardiac magnetic resonance (CMR) imaging and electrocardiogram (ECG) data. A sophisticated strategy to recover a patient's plausible Purkinje structure would enable these cardiac digital twins to augment clinical data and inform Purkinje-based risk stratification. This study presents and evaluates new computational techniques to infer physiological Purkinje terminal (root node) locations and timings and cardiac conduction properties from clinical CMR and ECG using Eikonal simulations. Our extended sequential Monte Carlo approximate Bayesian computation-based inference method shows an improved match in simulated QRS complexes to Q-wave morphologies in clinical 12-lead ECGs with Pearson's correlation coefficients of 0.89.