ECG Signal

Electrocardiograms (ECGs) are vital for diagnosing and monitoring cardiovascular health, with research focusing on improving accuracy and accessibility of analysis. Current efforts utilize deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), to enhance signal processing tasks such as noise reduction, signal completion, and arrhythmia detection. These advancements enable more accurate diagnoses from various ECG sources, including single-lead recordings from wearable devices, and facilitate the development of improved diagnostic tools and remote patient monitoring systems.