Before revision:  Methods: HSPs were generated using G3D basis functions and passed through a boundary element forward model to obtain corresponding BSPs . The generated BSPs (input) and HSPs (output) were used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs.
After revision:  Methods: HSPs were generated with single Gaussian basis functions at discrete heart surface locations and projected to corresponding BSPs using a volume conductor torso model. Both BSP (inputs) and HSP (outputs) were mapped to regular 2D surface meshes and used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs.