Smoothness and continuity of cost functionals for ECG mismatch computation
This work addresses a bottleneck in cardiac electrophysiology for researchers developing inverse models from ECG measurements, though it appears incremental as it builds on existing pipeline concepts.
The paper tackles the problem of smoothness in simulated ECGs relative to model parameters for inverse electrophysiological modeling, demonstrating key factors for efficient inverse modeling through smooth cost functionals using a simplified idealized left ventricle model.
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.