  Ventricular tachycardia (VT) can be one cause of sudden cardiac death
affecting 4.25 million persons per year worldwide. A curative treatment is
catheter ablation in order to inactivate the abnormally triggering regions. To
facilitate and expedite the localization during the ablation procedure, we
present two novel localization techniques based on convolutional neural
networks (CNNs). In contrast to existing methods, e.g. using ECG imaging, our
approaches were designed to be independent of the patient-specific geometries
and directly applicable to surface ECG signals, while also delivering a binary
transmural position. One method outputs ranked alternative solutions. Results
can be visualized either on a generic or patient geometry. The CNNs were
trained on a data set containing only simulated data and evaluated both on
simulated and clinical test data. On simulated data, the median test error was
below 3mm. The median localization error on the clinical data was as low as
32mm. The transmural position was correctly detected in up to 82% of all
clinical cases. Using the ranked alternative solutions, the top-3 median error
dropped to 20mm on clinical data. These results demonstrate a proof of
principle to utilize CNNs to localize the activation source without the
intrinsic need of patient-specific geometrical information. Furthermore,
delivering multiple solutions can help the physician to find the real
activation source amongst more than one possible locations. With further
optimization, these methods have a high potential to speed up clinical
interventions. Consequently they could decrease procedural risk and improve VT
patients' outcomes.
