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. Results: Fitted G3D basis function parameters can accurately reconstruct the real-world left ventricular paced recording with percent root mean squared error (RMSE) of 1.34 1.30\%.
After 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 . Predictive capabilities of the network were tested with unseen synthetic and experimental data. A dense full connected single hidden layer neural network was trained to map body surface impulses to heart surface Gaussian basis functions for reconstructing HSP. Synthetic pulses moving across the heart surface were predicted from the neural network with root mean squared error (RMSE) of 1.34 1.30\%.