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Related Experiment Video

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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning.

Md Shakil Zaman¹, Jwala Dhamala¹, Pradeep Bajracharya¹

¹Rochester Institute of Technology, Rochester, NY, United States.

Frontiers in Physiology

|November 11, 2021

Summary

This study introduces a Bayesian active learning method for accurate and efficient probabilistic estimation of cardiac electrophysiological model parameters, significantly reducing computational costs for personalized heart models.

Keywords:

Gaussian process cardiac electrophysiological model high-dimensional Bayesian optimization probabilistic parameter estimation variational autoencoder

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In Silico Clinical Trials for Cardiovascular Disease

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Published on: May 27, 2022

Area of Science:

Computational Biology
Biophysics
Cardiovascular Research

Background:

Probabilistic estimation of cardiac electrophysiological model parameters is crucial for personalized medicine and uncertainty quantification.
Direct Markov Chain Monte Carlo (MCMC) sampling is computationally intensive due to expensive model simulations.
Surrogate models offer computational efficiency but often lack sufficient accuracy for posterior probability density function (pdf) approximation.

Purpose of the Study:

To develop a novel Bayesian active learning method for direct approximation of cardiac model parameter posterior pdfs.
To enhance the accuracy and efficiency of parameter estimation in complex cardiac electrophysiological models.
To enable high-dimensional parameter inference at the resolution of cardiac mesh.

Main Methods:

Integration of a generative model within Bayesian active learning to handle high-dimensional parameter spaces.
Introduction of new acquisition functions designed to prioritize shape approximation over mode-finding for posterior pdfs.
Intelligent selection of training points to query the simulation model, minimizing sample requirements.

Main Results:

The proposed method demonstrated superior accuracy in approximating posterior pdfs compared to standard Bayesian active learning.
Significant reduction in computational cost was achieved relative to traditional and accelerated MCMC sampling techniques.
Effective estimation of tissue excitability in a 3D cardiac electrophysiological model was validated using synthetic and real data.

Conclusions:

The developed Bayesian active learning approach offers a computationally efficient and accurate solution for probabilistic parameter estimation in cardiac electrophysiology.
This method facilitates improved model personalization and uncertainty quantification for cardiac models.
The novel acquisition functions enhance the learning process for complex posterior probability distributions.