Smoothness and continuity of cost functionals for ECG mismatch computation

TL;DR

This paper investigates the smoothness of ECG simulation models with respect to physiological parameters, aiming to improve inverse modeling techniques in cardiac electrophysiology.

Contribution

It introduces a framework to analyze the smoothness of ECG cost functionals across the entire modeling pipeline, aiding the development of better inverse models.

Findings

Identifies key factors affecting smoothness in ECG simulation models

Demonstrates the importance of smooth cost functionals for inverse modeling

Provides insights for designing optimization algorithms in electrophysiology

Abstract

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.