Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study

TL;DR

This study demonstrates that deep learning models can predict three-dimensional electrical wave patterns in the heart from simulated cardiac motion data, offering a potential new diagnostic tool for arrhythmias.

Contribution

The paper introduces a deep learning approach trained on simulated data to accurately predict electrical wave dynamics from cardiac motion, even in complex and heterogeneous conditions.

Findings

Deep neural networks can reconstruct complex electrical wave patterns.

Models trained on one simulation method generalize to others.

Predictions remain accurate with scars and heterogeneity.

Abstract

The heart's contraction is caused by electrical excitation which propagates through the heart muscle. It was recently shown that the electrical excitation can be computed from the contractile motion of a simulated piece of heart muscle tissue using deep learning. In cardiac electrophysiology, a primary diagnostic goal is to identify electrical triggers or drivers of heart rhythm disorders. However, using electrical mapping techniques, it is currently impossible to map the three-dimensional morphology of the electrical waves throughout the entire heart muscle, especially during ventricular arrhythmias. Therefore, the approach to calculate or predict electrical excitation from the hearts motion could be a promising alternative diagnostic approach. Here, we demonstrate in computer simulations that it is possible to predict three-dimensional electrical wave dynamics from ventricular deformation mechanics using deep learning. We performed thousands of simulations of electromechanical activation dynamics in ventricular geometries and used the data to train a neural network which subsequently predicts the three-dimensional electrical wave pattern that caused the deformation. We demonstrate that, next to focal wave patterns, even complicated three-dimensional electrical wave patterns can be reconstructed, even if the network has never seen the particular arrhythmia. We show that the deep learning model has the ability to generalize by training it on data generated with the smoothed particle hydrodynamics (SPH) method and subsequently applying it to data generated with the finite element method (FEM). Predictions can be performed in the presence of scars and with significant heterogeneity. Our results suggest that, deep neural networks could be used to calculate intramural action potential wave patterns from imaging data of the motion of the heart muscle.