Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 19, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Improved performance of bayesian solutions for inverse electrocardiography using multiple information sources.

Yeşim Serinagaoglu1, Dana H Brooks, Robert S MacLeod

  • 1Electrical and Electronics Engineering Department, Middle East Technical University, Ankara 06530, Turkey. yserin@metu.edu.tr

IEEE Transactions on Bio-Medical Engineering
|October 6, 2006
PubMed
Summary

Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Are drivers recurring or ephemeral? observations from serial mapping of persistent atrial fibrillation.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology·2024
Same author

Transfer Learning for Improved Classification of Drivers in Atrial Fibrillation.

Computing in cardiology·2024
Same author

Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging.

Physiological measurement·2023
Same author

Investigation into the importance of using natural PVCs and pathological models for potential-based ECGI validation.

Frontiers in physiology·2023
Same author

Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations.

Computing in cardiology·2023
Same author

Improving the study of brain-behavior relationships by revisiting basic assumptions.

Trends in cognitive sciences·2023

Reconstructing cardiac electrical activity using inverse electrocardiography (ECG) is challenging. Incorporating sparse epicardial potential data with Bayesian methods significantly improves the accuracy of epicardial potential distribution reconstruction.

Area of Science:

  • Biomedical Engineering
  • Computational Electrophysiology
  • Medical Imaging

Background:

  • The inverse electrocardiography (ECG) problem aims to determine cardiac electrical sources from body surface measurements.
  • This problem is ill-posed due to signal attenuation and smoothing within the thorax, necessitating a priori constraints for stable solutions.
  • Current methods for reconstructing heart surface potentials lack clinical utility due to limited prior information and inadequate error metrics.

Purpose of the Study:

  • To improve the clinical utility of inverse ECG by developing a novel approach for reconstructing epicardial potential distributions.
  • To integrate statistical prior information and sparse epicardial measurements into a unified framework.
  • To evaluate the accuracy and confidence in reconstructed epicardial potentials using Bayesian methodology.

Related Experiment Videos

Last Updated: Jul 19, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Main Methods:

  • Employed a Bayesian methodology to combine body surface ECG measurements, standard forward models, statistical priors of epicardial potentials, and sparse epicardial potentials from coronary venous catheters.
  • Conducted simulation studies to compare reconstruction accuracy with varying information sources and numbers of epicardial measurements.
  • Utilized Bayesian error covariance and traditional metrics like relative error for accuracy evaluation.

Main Results:

  • Including sparsely sampled epicardial potential data from coronary venous catheters substantially enhanced the reconstruction accuracy of epicardial potential distributions.
  • The Bayesian framework effectively incorporated diverse information sources, demonstrating feasibility for inverse ECG problems.
  • Bayesian error standard deviations provided a reliable measure of confidence in the reconstructed results, even without validation data.

Conclusions:

  • A Bayesian approach integrating statistical priors and sparse epicardial measurements offers a feasible and effective solution for the ill-posed inverse ECG problem.
  • Sparse epicardial potential data acquisition via coronary venous catheters can significantly improve the reconstruction of cardiac electrical activity.
  • The developed methodology provides a robust framework for assessing confidence in inverse ECG solutions, paving the way for potential clinical applications.