# Inverse Mechano-Electrical Reconstruction of Cardiac Excitation Wave Patterns from Mechanical Deformation using Deep Learning

arXiv: https://arxiv.org/abs/2008.01640 · HTML: https://mlsanity.com/paper/2008.01640/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networks](https://mlsanity.com/paper/2109.10472/) (2021)
- [Reconstruction of Three-dimensional Scroll Waves in Excitable Media from Two-Dimensional Observations using Deep Neural Networks](https://mlsanity.com/paper/2209.06860/) (2022)
- [Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study](https://mlsanity.com/paper/2305.07822/) (2023)
- [Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models](https://mlsanity.com/paper/2312.14830/) (2023)
- [Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks](https://mlsanity.com/paper/2603.03832/) (2026)

## Abstract (arXiv)

The inverse mechano-electrical problem in cardiac electrophysiology is the attempt to reconstruct electrical excitation or action potential wave patterns from the heart's mechanical deformation that occurs in response to electrical excitation. Because heart muscle cells contract upon electrical excitation due to the excitation-contraction coupling mechanism, the resulting deformation of the heart should reflect macroscopic action potential wave phenomena. However, whether the relationship between macroscopic electrical and mechanical phenomena is well-defined and furthermore unique enough to be utilized for an inverse imaging technique, in which mechanical activation mapping is used as a surrogate for electrical mapping, has yet to be determined. Here, we provide a numerical proof-of-principle that deep learning can be used to solve the inverse mechano-electrical problem in phenomenological two- and three-dimensional computer simulations of the contracting heart wall, or in elastic excitable media, with muscle fiber anisotropy. We trained a convolutional autoencoder neural network to learn the complex relationship between electrical excitation, active stress, and tissue deformation during both focal or reentrant chaotic wave activity, and consequently used the network to succesfully estimate or reconstruct electrical excitation wave patterns from mechanical deformation in sheets and bulk-shaped tissues, even in the presence of noise and at low spatial resolutions. We demonstrate that even complicated three-dimensional electrical excitation wave phenomena, such as scroll waves and their vortex filaments, can be computed with very high reconstruction accuracies of about 95% from mechanical deformation using autoencoder neural networks, and we provide a comparison with results that were obtained previously with a physics- or knowledge-based approach.