In recent times, there has been growing attention towards Computer-Aided Diagnosis (CAD) systems dedicated to the automated analysis of electrocardiograms (ECGs). These systems have gained significant interest due to their potential to facilitate the diagnostic process and improve the precision of cardiac anomaly identification and classification. Furthermore, CAD systems can be situated within the broader context of telemedicine, specifically telecardiology and remote monitoring of cardiac conditions. It is within this context that this thesis work is situated, presenting a method that employs Artificial Intelligence (AI) for the automatic classification of cardiac anomalies. It is based on the electrocardiographic traces (ECG) acquired through a three-lead ECG wearable patch device, commercialised by CGM and designed in collaboration with STMicroelectronics, called 'HI ECG 3-LEAD'.