Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study

Abstract

Atrial fibrillation is the most common rhythm disorder of the heart associated with a rapid and irregular beating of the upper chambers. Activation mapping remains the gold standard to diagnose and interpret atrial fibrillation. However, fibrillatory activation maps are highly sensitive to far-field effects, and often disagree with other optical mapping modalities. Here we show that computational modeling can identify spurious non-local components of atrial fibrillation electrograms and improve activation mapping. We motivate our approach with a cohort of patients with potential drivers of persistent atrial fibrillation. In a computational study using a monodomain Maleckar model, we demonstrate that in organized rhythms, electrograms successfully track local activation, whereas in atrial fibrillation, electrograms are sensitive to spiral wave distance and number, spiral tip trajectories, and effects of fibrosis. In a clinical study, we analyzed n = 15 patients with persistent atrial fibrillation that was terminated by limited ablation. In five cases, traditional activation maps revealed a spiral wave at sites of termination; in ten cases, electrogram timings were ambiguous and activation maps showed incomplete reentry. By adjusting electrogram timing through computational modeling, we found rotational activation, which was undetectable with conventional methods. Our results demonstrate that computational modeling can identify non-local deflections to improve activation mapping and explain how and where ablation can terminate persistent atrial fibrillation. Our hybrid computational/physiological approach has the potential to optimize map-guided ablation and improve ablation therapy in atrial fibrillation.

Physics-Informed Neural Networks for Cardiac Activation Mapping

“…For localized re-entry or fibrillation, however, we expect that the method will not work, as the conduction velocity of the spiral wave depends on both time and space and the Eikonal equation does not hold. As a next step, we plan to test this methodology in a cohort of patients with focal activations or macro re-entry tachycardia [15].…”

“…Here, we propose to use a physics-informed neural network to create activation maps of the atria [15], more accurately and efficiently than with linear interpolation alone. We incorporate the physical knowledge using the Eikonal equation, which describes the behavior of the activation times for a conduction velocity field.…”

Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator

“…To study how different cell types interact in the presence of drugs, we consider a simple cable model. We discretize a one-dimensional version of the set of continuum equations (1) to (5) in space and time using finite differences and adopt an explicit time integrator using our in-house code [52]. The cable model is 1cm long, similar to a transmural section of the ventricular wall, and is discretized with 100 elements of 100μm length, similar to the length of a healthy adult cardiac myocyte.…”

Independent mapping methods reveal rotational activation near pulmonary veins where atrial fibrillation terminates before pulmonary vein isolation

“…Known epicardial–endocardial differences in AF from optical imaging of human atria may explain less stable activity on body surface mapping or surgical epicardial maps. Recent work shows that traditional surgical mapping by marking electrograms may encounter difficulties in the face of complex electrograms, and that computational‐guidance may assist activation marking of AF in such cases …”