Cardiovascular disease poses a significant health risk and remains one of the leading causes of death worldwide. The 12-lead electrocardiogram is a comprehensive and widely accessible diagnostic tool for identifying cardiac abnormalities. Early and accurate diagnosis allows for timely treatment and intervention, helping to prevent severe complications. It is therefore crucial to have automated classification tools capable of recognizing these cardiac alterations, streamlining clinical practice, which is currently very time-consuming. This thesis contributes to this context by building on a solution developed by a team ranked third in the PhysioNet/Computing in Cardiology Challenge 2020, which aimed to classify 27 cardiac arrhythmias using a new scoring metric.