Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a hereditary heart muscle disease, causing predisposition to ventricular arrhythmia and sudden cardiac death. The early detection of the disease and signs of progression is of great importance and can save lives. This work focuses on data processing, using Deep Learning (DL) to evaluate and analyze electrocardiographic signal collected through an innovative body surface mapping system on a small dataset of 40 patients, in order to formulate a criteria for the diagnosis of ARVC. Each patient underwent 10 minutes of recording with a novel non-invasive electrocardiographic body surface mapping (ECG-BSM) technique, that uses a 252-unipolar leads vest, and enables the recording of multiple electrograms from the whole thorax and a reconstruction of epicardial potentials.