The project aims to advance cardiac health diagnostics through two critical tasks: irregular heartbeat classification and activation map reconstruction using ECG signals. Utilizing the ECG Heartbeat Categorization Dataset, the project focuses on developing a robust multiclass classification model to accurately diagnose various irregular heartbeats, facilitating early detection and timely treatment of cardiac conditions. In parallel, the project employs a neural network to transform ECG sequences into activation maps, using simulated intracardiac transmembrane voltage recordings. This reconstruction task is designed to deepen our understanding of the heart’s electrical activity, offering detailed insights that can lead to more precise and effective clinical interventions.