Electrocardiogram Generation

Electrocardiogram (ECG) generation focuses on creating realistic synthetic ECG signals using various machine learning techniques to address data scarcity, privacy concerns, and the need for large, diverse training datasets in cardiac disease diagnosis. Current research emphasizes the use of diffusion models, generative adversarial networks (GANs), and structured state-space models, often incorporating physiological insights through ordinary differential equations or statistical shape priors to enhance the realism and clinical utility of the generated ECGs. This work is significant because it enables the development of more robust and accurate deep learning models for ECG analysis, potentially improving the diagnosis and management of cardiovascular diseases while respecting patient data privacy.