  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.
