Electrocardiogram Data

Electrocardiogram (ECG) data analysis focuses on extracting meaningful information from heart's electrical activity to diagnose cardiac and increasingly, non-cardiac conditions. Current research emphasizes developing and optimizing deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs), for tasks such as arrhythmia classification, disease detection, and even laboratory value estimation. These advancements aim to improve diagnostic accuracy, efficiency, and accessibility, particularly in resource-constrained settings, by leveraging techniques like self-supervised learning and transfer learning to address data limitations. The ultimate goal is to enhance patient care through faster, more accurate, and cost-effective cardiovascular assessments.