Before revision:  Activation map produced from the predicted time series had a RMSE of 17.0\% and mean absolute difference of 10.3 10.8ms when compared to that produced from the actual left ventricular paced recording. Conclusion: A Gaussian basis function based data driven model for re-framing the inverse problem of electrocardiography as a regression problem is successful and produces promising time series and activation map predictions of real-world recordings even when only trained using Guassian data. Significance: The HSPs predicted by the neural network can be used to create activation maps to identify cardiac dysfunctions during clinical assessment.
After revision:  Activation map produced from the predicted time series had a RMSE of 17.0\% and mean absolute difference of 10.3 of 9.1