Synthetic Electrocardiogram

Synthetic electrocardiogram (ECG) generation focuses on creating realistic artificial ECG signals for various applications, primarily addressing data scarcity and privacy concerns in training machine learning models for cardiac diagnosis. Current research emphasizes developing sophisticated generative models, including generative adversarial networks (GANs), diffusion models, and those incorporating ordinary differential equations (ODEs) to capture the complex physiological dynamics of the heart, often augmented by incorporating clinical text reports or other modalities. These advancements enable improved training of diagnostic algorithms, facilitate data augmentation for imbalanced datasets, and provide valuable tools for digitizing legacy ECG data, ultimately enhancing the accuracy and accessibility of cardiac analysis.