Abstract
Abstract: When there is a suspicion of a heart attack, an electrocardiogram (ECG) is a crucial test. It measures the electrical activity of the heart, which is manifested through small electric impulses when the heart beats. The subsequent process of analyzing ECG patterns is time-consuming but vital in determining the likelihood of cardiovascular disease by medical professionals. This project utilizes ECG image data to automate the interpretation of ECG recordings, aiming to assist clinicians in detecting life-threatening Myocardial Infarction. By taking an ECG image as input, the system classifies and attempts to categorize the final result into five classes: Non-Ectopic Beats, Supraventricular Ectopic Beats, Ventricular Ectopic Beats, Fusion Beats, and Unknown Beats.
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