Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning
This work addresses the challenge of model personalization and uncertainty quantification in cardiac electrophysiology, offering a more efficient solution for researchers and clinicians, though it is incremental as it builds on existing Bayesian active learning techniques.
The paper tackles the computationally intensive problem of estimating posterior probability density functions for cardiac electrophysiological model parameters by proposing a Bayesian active learning method that intelligently selects training points to approximate the posterior with high accuracy using fewer samples. The result shows improved accuracy over standard methods and substantially reduced computational cost compared to MCMC sampling.
Probabilistic estimation of cardiac electrophysiological model parameters serves an important step towards model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this paper, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.