POD-enhanced deep learning-based reduced order models for the real-time simulation of cardiac electrophysiology in the left atrium
The numerical simulation of multiple scenarios easily becomes computationally prohibitive
for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models …
for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models …
EP-PINNs: Cardiac electrophysiology characterisation using physics-informed neural networks
Accurately inferring underlying electrophysiological (EP) tissue properties from action
potential recordings is expected to be clinically useful in the diagnosis and treatment of …
potential recordings is expected to be clinically useful in the diagnosis and treatment of …
Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning
Modelling complex systems, like the human heart, has made great progress over the last
decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias …
decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias …
Deep learning for model correction in cardiac electrophysiological imaging
V Kashtanova, I Ayed, A Arrieula… - … on Medical Imaging …, 2022 - proceedings.mlr.press
Imaging the electrical activity of the heart can be achieved with invasive catheterisation.
However, the resulting data are sparse and noisy. Mathematical modelling of cardiac …
However, the resulting data are sparse and noisy. Mathematical modelling of cardiac …
Few-shot generation of personalized neural surrogates for cardiac simulation via bayesian meta-learning
Clinical adoption of personalized virtual heart simulations faces challenges in model
personalization and expensive computation. While an ideal solution is an efficient neural …
personalization and expensive computation. While an ideal solution is an efficient neural …
Hyper-EP: Meta-learning hybrid personalized models for cardiac electrophysiology
X Jiang, S Vadhavkar, Y Ye, M Toloubidokhti… - arXiv preprint arXiv …, 2024 - arxiv.org
Personalized virtual heart models have demonstrated increasing potential for clinical use,
although the estimation of their parameters given patient-specific data remain a challenge …
although the estimation of their parameters given patient-specific data remain a challenge …
Physics-informed Fully Connected and Recurrent Neural Networks for Cardiac Electrophysiology Modelling
I Nazarov, I Olakorede, A Qureshi… - 2022 Computing in …, 2022 - ieeexplore.ieee.org
Cardiovascular diseases, the leading cause of death and disability, are often underlined by
cardiac arrhythmias. Cardiac electrophysiology models play an increasingly important role …
cardiac arrhythmias. Cardiac electrophysiology models play an increasingly important role …
Neural State-Space Modeling with Latent Causal-Effect Disentanglement
Despite substantial progress in deep learning approaches to time-series reconstruction, no
existing methods are designed to uncover local activities with minute signal strength due to …
existing methods are designed to uncover local activities with minute signal strength due to …
A deep learning-based hybrid computational approach to cardiac electrophysiology
AF Kuloğlu - 2023 - open.metu.edu.tr
Electrophysiological modeling of the heart has witnessed significant progress with the
increase of available computational power. Realistic electrophysiology models often require …
increase of available computational power. Realistic electrophysiology models often require …
Learning Cardiac Electrophysiology Dynamics with PDE-based Physiological Constraints for Data-Driven Personalised Predictions
V Kashtanova - 2023 - hal.science
A current major scientific challenge consists in combining the versatility of intensive data-
based approaches with the physically grounded modelling approaches developed in …
based approaches with the physically grounded modelling approaches developed in …