부산대학교 도서관

Heart attack, medically termed Myocardial Infarction (MI), happens when the heart muscle sustains damage due to insufficient blood flow. MI ranks as the foremost contributor to death among middle-aged and elderly individuals on a global scale. AI-based approaches have the potential to automatically diagnose MI by leveraging Electrocardiogram (ECG) signals. In this study, a comprehensive review is conducted to thoroughly evaluate Machine Learning (ML) and Deep Learning (DL) models, in identifying myocardial infarction (MI) through the analysis of ECG signals. The manual extraction of features and the selection of ECG signals are necessitated by traditional machine learning approaches, whereas these tasks are automated by deep learning models. Remarkably, Deep CNN (DCNNs) have demonstrated outstanding classification capabilities in the diagnosis of MI, leading to their increasing prominence in recent times.