PUBLICATIONS
Cardiac fibers play an essential role in the electrical and mechanical function of the heart, making them a critical parameter in cardiac modeling. However, identifying the 3D arrangement of the fibers in clinical practice still encounters some limitations. Solving the inverse problem of inferring the fiber orientations from electrophysiological data has been reported as a potential method for identifying patient-specific fiber orientations. In this work, we employ -Fibernet, a recently proposed Physics-Informed Neural Network (PINN) model, to reconstruct the fiber orientations on the right ventricle’s endocardial surface from a single activation map. The model equips the traditional PINN framework with an ensemble of parallel neural networks to cope with the uncertainty in the fiber approximations. Each ensemble member estimates the activation times and fiber orientations. The best fiber orientations are finally selected using a specific method to reduce the uncertainty of predictions. We evaluated the performance of the model in a simple propagation pattern and a more realistic propagation pattern incorporating the influence of the Purkinje network. Our results indicate the robustness of -Fibernet and its capacity to learn complex activation patterns and fiber distributions while trained with only a single activation map.
Cardiac digital twins have shown promise to personalize treatments. However, there are multiple challenges to incorporate patient-specific information from non-invasive data. For instance, recovering the activation sequence in atria from the standard electrocardiogram (ECG) remains elusive. Recent studies have tackled this task on the ventricles, where the ECG signal is much stronger. This work presents a novel methodology to recover the atrial electrical activity with physics-informed neural networks. Instead of focusing on the activation times, we predict the direction of propagation of the electrical wave at each point with a neural network. Then, by solving a linear system for the Poisson equation, we recover the activation times that satisfy the anisotropic eikonal equation. The proposed methodology is compared with a methodology that predicts directly the electrical propagation and does not enforce the propagation model. We compare it to a traditional physics-informed neural network formulation, where the eikonal equation is only weakly imposed. We validate our methodology in a biatrial synthetic case using realistic lead fields for ECG calculation. We then learn the activation sequence from patient data, recovering a physiological activation pattern. We believe this is a first step toward digital twinning of the atria.
Edificio de Innovación UC, Piso 2
Vicuña Mackenna 4860
Macul, Chile
Vicuña Mackenna 4860
Macul, Chile