ECGInsight: A Web Application-Based Approach to Myocardial Infarction Detection From ECG Image Reports Utilizing ResNet

Cardiovascular illness is frequently the cause of my-ocardial infarction (MI), also referred to as a heart attack, which happens when the blood supply to a portion of the heart muscle is interrupted or diminished. This event can range from being asymptomatic to causing significant declines in cardiovascular function and premature mortality. An electrocardiogram (ECG) is vital for diagnosing and assessing MI, as it measures the heart's electrical activity and can detect various cardiac conditions. In this study, we developed a Residual Network (ResNet)-based model for MI detection using ECG signals. The model was trained on the PTB-XL dataset, which includes 21,837 annotated ECG signals, and tested on converted ECG signals from the Mendeley Data ECG Images dataset. Our model achieved high precision, recall, and F1-scores, demonstrating robust performance with an accuracy of 98.95 % on the validation set and 98.63 % on the test set. In addition, we developed ECGInsight, a web application that lets users take or upload a picture of an ECG report. The photo is converted to ECG signals, which are then processed by ResNet to produce predictions with confidence levels. This study underscores the potential of Deep Learning (DL) models in automated MI detection and highlights the practical utility of the ECGInsight web application in making MI detection accessible to users, ultimately contributing to advancements in cardiovascular healthcare.