# Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study

arXiv: https://arxiv.org/abs/2305.07822 · HTML: https://mlsanity.com/paper/2305.07822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Inverse Mechano-Electrical Reconstruction of Cardiac Excitation Wave Patterns from Mechanical Deformation using Deep Learning](https://mlsanity.com/paper/2008.01640/) (2020)
- [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)

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

- [Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models](https://mlsanity.com/paper/2312.14830/) (2023)

## Abstract (arXiv)

The heart's contraction is caused by electrical excitation which propagates through the heart muscle. It was recently shown that the electrical excitation can be computed from the contractile motion of a simulated piece of heart muscle tissue using deep learning. In cardiac electrophysiology, a primary diagnostic goal is to identify electrical triggers or drivers of heart rhythm disorders. However, using electrical mapping techniques, it is currently impossible to map the three-dimensional morphology of the electrical waves throughout the entire heart muscle, especially during ventricular arrhythmias. Therefore, the approach to calculate or predict electrical excitation from the hearts motion could be a promising alternative diagnostic approach. Here, we demonstrate in computer simulations that it is possible to predict three-dimensional electrical wave dynamics from ventricular deformation mechanics using deep learning. We performed thousands of simulations of electromechanical activation dynamics in ventricular geometries and used the data to train a neural network which subsequently predicts the three-dimensional electrical wave pattern that caused the deformation. We demonstrate that, next to focal wave patterns, even complicated three-dimensional electrical wave patterns can be reconstructed, even if the network has never seen the particular arrhythmia. We show that the deep learning model has the ability to generalize by training it on data generated with the smoothed particle hydrodynamics (SPH) method and subsequently applying it to data generated with the finite element method (FEM). Predictions can be performed in the presence of scars and with significant heterogeneity. Our results suggest that, deep neural networks could be used to calculate intramural action potential wave patterns from imaging data of the motion of the heart muscle.