Variational autoencoder-based neural electrocardiogram synthesis trained by FEM-based heart simulator.

Background: For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation.

Objective: The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters.