Paper Electrocardiogram

Paper electrocardiograms (ECGs) present a significant challenge for modern digital healthcare, requiring digitization to leverage advanced analysis techniques. Current research focuses on developing robust deep learning models, including U-Net and convolutional neural networks (CNNs), to accurately extract and reconstruct ECG signals from images of paper records, often addressing issues like incomplete data and image artifacts. These advancements enable the application of AI for automated diagnosis of cardiac abnormalities and other conditions, potentially improving access to accurate and timely cardiac assessments, particularly in resource-limited settings.