  The analysis of electrical impulse phenomena in cardiac muscle tissue is
important for the diagnosis of heart rhythm disorders and other cardiac
pathophysiology. Cardiac mapping techniques acquire local temporal measurements
and combine them to visualize the spread of electrophysiological wave phenomena
across the heart surface. However, low spatial resolution, sparse measurement
locations, noise and other artifacts make it challenging to accurately
visualize spatio-temporal activity. For instance, electro-anatomical catheter
mapping is severely limited by the sparsity of the measurements, and optical
mapping is prone to noise and motion artifacts. In the past, several approaches
have been proposed to obtain more reliable maps from noisy or sparse mapping
data. Here, we demonstrate that deep learning can be used to compute phase maps
and detect phase singularities in optical mapping videos of ventricular
fibrillation, as well as in very noisy, low-resolution and extremely sparse
simulated data of reentrant wave chaos mimicking catheter mapping data. The
deep learning approach learns to directly associate phase maps and the
positions of phase singularities with short spatio-temporal sequences of
electrical data. We tested several neural network architectures, based on a
convolutional neural network with an encoding and decoding structure, to
predict phase maps or rotor core positions either directly or indirectly via
the prediction of phase maps and a subsequent classical calculation of phase
singularities. Predictions can be performed across different data, with models
being trained on one species and then successfully applied to another, or being
trained solely on simulated data and then applied to experimental data. Future
uses may include the analysis of optical mapping studies in basic
cardiovascular research, as well as the mapping of atrial fibrillation in the
clinical setting.
