  The field of cardiac electrophysiology tries to abstract, describe and
finally model the electrical characteristics of a heartbeat. With recent
advances in cardiac electrophysiology, models have become more powerful and
descriptive as ever. However, to advance to the field of inverse
electrophysiological modeling, i.e. creating models from electrical
measurements such as the ECG, the less investigated field of smoothness of the
simulated ECGs w.r.t. model parameters need to be further explored. The present
paper discusses smoothness in terms of the whole pipeline which describes how
from physiological parameters, we arrive at the simulated ECG. Employing such a
pipeline, we create a test-bench of a simplified idealized left ventricle model
and demonstrate the most important factors for efficient inverse modeling
through smooth cost functionals. Such knowledge will be important for designing
and creating inverse models in future optimization and machine learning
methods.
