DeRC_ECG: Combined UNet-ResNet Framework for Automated Denoising and Classification of Noisy Paper-Based ECGs

As part of the George B. Moody PhysioNet Challenge 2024, we developed a computational approach, leveraging a combination of UNet and ResNet models, to analyze and classify electrocardiogram (ECG) images.Our team, DeRC ECG, introduced an innovative method that applies UNet for denoising ECG images, followed by the use of ResNet18 Networks and other CNN-based models to accurately classify ECGs into various diagnostic categories.For the classification task, our approach achieved a macro F-measure of 0.359 on the official test set.