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Found 6 papers, 2 papers with code
Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
We characterized and compared the diffusion-generated solutions to solutions obtained with biophysical models and found that diffusion models learn to replicate spiral and scroll waves dynamics so well that they could serve as an alternative data-driven approach for the modeling of excitation waves in cardiac tissue.
Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study
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.
Reconstruction of Three-dimensional Scroll Waves in Excitable Media from Two-Dimensional Observations using Deep Neural Networks
In the future, deep neural networks could be used to visualize intramural action potential wave patterns from epi- or endocardial measurements.
Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networks
Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data.
Inverse Mechano-Electrical Reconstruction of Cardiac Excitation Wave Patterns from Mechanical Deformation using Deep Learning
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.
Parallel Statistical Multi-resolution Estimation
We show that statistical multi-resolution estimation can enhance the resolution improvement of the plain SOFI algorithm just as the Fourier-reweighting of SOFI.
