DeRC_ECG: Combined UNet-ResNet Framework for Automated Denoising and Classification of Noisy Paper-Based ECGs
Year: 2024
Type: conference-paper
Abstract: 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.
Source: Computing in cardiology
Authors: Weijie Sun, Siqi Cao, Sunil V. Kalmady, Md Saiful Islam, Tayyib Ul Hassan, Abram Hindle, Russell Greiner, Padma Kaul
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405435343