MCMC for Bayesian inference of the non-conducting region in intra-atrial reentrant tachycardia

Explore advanced Markov chain Monte Carlo (MCMC) methods for Bayesian inference in cardiac electrophysiology modeling through this 43-minute conference talk. Learn how to estimate non-conducting regions in intra-atrial reentrant tachycardia using patient-specific cardiac models and digital twins. Discover the challenges of cardiac arrhythmia modeling, where abnormal electrical activity varies between individuals and requires personalized treatment approaches. Understand how current imaging limitations necessitate uncertainty quantification during model calibration. Examine the application of Bayesian inference for parameter estimation in cardiac electrophysiology models, focusing on two-dimensional cardiac tissue simulations that model electrical signal propagation coordinating heart contractions. Investigate techniques for estimating shape parameters of electrically inactive regions or scars from mapping catheter data, which disrupt signal propagation and can lead to arrhythmia. Master an efficient MCMC approach that minimizes required samples through summary statistics-based likelihood functions and adaptive proposal distributions. Learn strategies for accounting for discretization errors through likelihood variance inflation and tailored meshing approaches in forward simulations. Discover computational efficiency improvements through multilevel methods that replace expensive simulations with cheaper approximations in the accept-reject step.