# Smoothness and continuity of cost functionals for ECG mismatch computation

arXiv: https://arxiv.org/abs/2201.04487 · HTML: https://mlsanity.com/paper/2201.04487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Fast Tetrahedral Meshing in the Wild](https://mlsanity.com/paper/1908.03581/) (2019)
- [Inference of ventricular activation properties from non-invasive electrocardiography](https://mlsanity.com/paper/2010.15214/) (2020)
- [GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models](https://mlsanity.com/paper/2102.09962/) (2021)

## Built on by (most cited first, 1 of 1)

- [Probabilistic learning of the Purkinje network from the electrocardiogram](https://mlsanity.com/paper/2312.09887/) (2023)

## Abstract (arXiv)

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.