Predicting Plausible Human Purkinje Network Morphology from Simulations

Abstract

The Purkinje network (PN) gains more clinically importance as it becomes target for pacing in rate control and defibrillation. However, our understanding of the PN morphology arises from animal experiments, which might not transfer to humans. Therefore, we propose an automated computer simulation predicting physiological PN morphologies depending on the heart shape. It starts by generating virtual heart shapes from a statistical shape atlas and generates virtual PNs on the endocardial surface. For the combined virtual models the eikonal equation is solved to estimate the local activation times throughout the myocardium, which then feed forward to an simulation of the 12-lead surface ECG. From the simulated ECG the QRS-complex is compared against a healthy standard QRS-complex ,which allows to estimate how physiological a PN morphology is. In our model, only bundle branch bifurcation points near the base or near the apex result in physiological QRS wave forms. For the right bundle, more physiological QRS waves can be obtained when the branching point is at the apex. Only a minor dependency of the ECG on the heart shape is found. However, a strong correlation between the bundle branch bifurcation points themselves is observed.