Deep learning model for ECG reconstruction reveals the information content of ECG leads

This study introduces a deep learning model based on the U-net architecture to reconstruct missing leads in electrocardiograms (ECGs). Using publicly available datasets, the model was trained to regenerate 12-lead ECG data from reduced lead configurations, demonstrating high accuracy in lead reconstruction. The results highlight the ability of the model to quantify the information content of each ECG lead and their inter-lead correlations. This has significant implications for optimizing lead selection in diagnostic scenarios, particularly in settings where full 12-lead ECGs are impractical. Additionally, the study provides insights into the physiological underpinnings of ECG signals and their propagation. The findings pave the way for advancements in telemedicine, portable ECG devices, and personalized cardiac diagnostics by reducing redundancy and enhancing signal interpretation.