EP-Net: learning cardiac electrophysiology models for physiology-based constraints in data-driven predictions
Cardiac electrophysiology (EP) models achieved good pro gress in simulating cardiac
electrical activity. However numerical issues and computational times hamper clinical …
electrical activity. However numerical issues and computational times hamper clinical …
APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics
Biophysically detailed mathematical modeling of cardiac electrophysiology is often
computationally demanding, for example, when solving problems for various patient …
computationally demanding, for example, when solving problems for various patient …
EP-Net 2.0: Out-of-domain generalisation for deep learning models of cardiac electrophysiology
Cardiac electrophysiology models achieved good progress in simulating cardiac electrical
activity. However, it is still challenging to leverage clinical measurements due to the …
activity. However, it is still challenging to leverage clinical measurements due to the …
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 …
Fully automated electrophysiological model personalisation framework from CT imaging
N Cedilnik, J Duchateau, F Sacher, P Jaïs… - Functional Imaging and …, 2019 - Springer
There has been a recent growing interest for cardiac computed tomography (CT) imaging in
the electrophysiological community. This imaging modality indeed allows to locate and …
the electrophysiological community. This imaging modality indeed allows to locate and …
[HTML][HTML] Validation and trustworthiness of multiscale models of cardiac electrophysiology
P Pathmanathan, RA Gray - Frontiers in Physiology, 2018 - frontiersin.org
Computational models of cardiac electrophysiology have a long history in basic science
applications and device design and evaluation, but have significant potential for clinical …
applications and device design and evaluation, but have significant potential for clinical …
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 …
[HTML][HTML] Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
We review some of the latest approaches to analysing cardiac electrophysiology data using
machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial …
machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial …
Modeling and registration for electrophysiology procedures based on three-dimensional imaging
K Rhode, M Sermesant - Current Cardiovascular Imaging Reports, 2011 - Springer
Computer models of cardiac electrophysiology (EP) can help to better understand the
mechanisms of arrhythmias and to guide interventions. However, model adjustment to …
mechanisms of arrhythmias and to guide interventions. However, model adjustment to …
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 …