12 Lead

Twelve-lead electrocardiograms (ECGs) are the gold standard for diagnosing cardiovascular diseases, but their acquisition can be cumbersome. Current research focuses on improving ECG analysis through deep learning models, including convolutional neural networks, recurrent neural networks (like LSTMs), and generative adversarial networks (GANs), to address challenges like reconstructing 12-lead ECGs from fewer leads, improving the accuracy of interval estimations, and enabling automated analysis from diverse data sources. These advancements aim to enhance diagnostic capabilities, facilitate remote monitoring, and improve the efficiency and accessibility of cardiovascular assessments.