The increasing demand for low-cost, non-invasive systems capable of monitoring electrocardiogram (ECG) traces in non-hospital settings has consequently led to a rapid evolution of intelligent medical decision support systems for the automatic analysis of ECGs. These systems detect and classify cardiac anomalies early, reducing healthcare professionals' workload and improving diagnostic capabilities. This thesis focuses on developing and comparing two anomaly classification models in the ECG medical context using Machine Learning (ML) and Deep Learning (DL). Both models have been used on signals acquired from the CGM Hi 3 Leads ECG (Hi-ECG), a wearable ECG device commercialised by CGM and co-developed with STMicroelectronics.