  Electroanatomic mapping as routinely acquired in ablation therapy of
ventricular tachycardia is the gold standard method to identify the
arrhythmogenic substrate. To reduce the acquisition time and still provide maps
with high spatial resolution, we propose a novel deep learning method based on
graph convolutional neural networks to estimate the depolarization time in the
myocardium, given sparse catheter data on the left ventricular endocardium,
ECG, and magnetic resonance images. The training set consists of data produced
by a computational model of cardiac electrophysiology on a large cohort of
synthetically generated geometries of ischemic hearts. The predicted
depolarization pattern has good agreement with activation times computed by the
cardiac electrophysiology model in a validation set of five swine heart
geometries with complex scar and border zone morphologies. The mean absolute
error hereby measures 8 ms on the entire myocardium when providing 50\% of the
endocardial ground truth in over 500 computed depolarization patterns.
Furthermore, when considering a complete animal data set with high density
electroanatomic mapping data as reference, the neural network can accurately
reproduce the endocardial depolarization pattern, even when a small percentage
of measurements are provided as input features (mean absolute error of 7 ms
with 50\% of input samples). The results show that the proposed method, trained
on synthetically generated data, may generalize to real data.
