Automated Customization of Cardiac Electrophysiology Models to Facilitate Patient-Specific Modeling
Year: 2023
Type: conference-paper
Abstract: Models of cardiac electrophysiology can be useful for assessing the behavior of the heart when subjected to interventions like pacing, defibrillation, or drugs.However, for patient-specific predictions, models must be customized to match the electrophysiological properties of individuals.We present a browser-based tool for customizing models of cardiac action potentials by fitting parameter values to user-provided datasets.The tool uses a particle swarm optimization algorithm accelerated by the user's graphics card, which can support a large particle population and typically finds a low-error solution within a small number of iterations (10-32), computed within seconds.The interface allows all model parameters or a user-specified subset to be selected for fitting, and the user can set bounds for all parameters to constrain their values.We demonstrate the effectiveness of this tool by creating parameterizations of a four-variable human ventricular model to match human cardiac action potential data taken from tissue exhibiting Brugada syndrome.We expect this tool will be useful for tuning models to match data recorded from individual experiments and patients under normal and diseased conditions.
Source: Computing in cardiology
Institution: Georgia Institute of Technology
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: Collaborative Research: Developing a Quantitative Three-Dimensional Understanding of Cardiac Arrhythmias, CPS: Frontier: Collaborative Research: Compositional, Approximate, and Quantitative Reasoning for Medical Cyber-Physical Systems, Integrative High-Resolution Experimental and Multiscale Modeling of Arrhythmias to Optimize Low Energy Anti-fibrillation Pacing (LEAP), Integrative and Quantitative Biosciences Accelerated Training Environment
OpenAlex ID: W4390453155