Generative AI for Minimal-Lead ECG Reconstruction

This project focuses on developing generative deep learning models to reconstruct multi-lead electrocardiogram signals from a limited set of input ECG leads. By learning the relationships among ECG leads, the model aims to evaluate how accurately clinically meaningful cardiac signal information can be recovered from minimal-lead recordings. The project assesses reconstruction performance from both a signal-fidelity perspective and a clinical relevance perspective, including the preservation of diagnostically important ECG patterns. More broadly, this work explores whether a small number of key ECG leads can capture sufficient information to support robust ECG interpretation, scalable monitoring, and future AI-enabled clinical applications.