Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...

Cardiac Action Potential

Cardiac Action Potential

Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials

You might also read

Related Articles

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

Sort by

Same author

Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.

Heart rhythm O2·2026

Same author

Clinical evaluation of a spot-check ECG for the diagnosis of atrial fibrillation.

Heart rhythm O2·2026

Same author

Uncertainty quantification of conduction velocity in models of cardiac spread of activation.

Medical & biological engineering & computing·2026

Same author

Predicting Ventricular Arrhythmia in Myocardial Ischemia Using Machine Learning.

Computing in cardiology·2026

Same author

Comparison of LGE MRI Scar Identification Methods for Atrial Computational Modeling.

Computing in cardiology·2026

Same author

Corrigendum: Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform (2020<i>Physiol. Meas.</i>41 015002).

Physiological measurement·2026

Search research articles

Related Experiment Video

Updated: Oct 5, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Reconstruction of cardiac position using body surface potentials.

Jake A Bergquist¹, Jaume Coll-Font², Brian Zenger³

¹Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States.

Computers in Biology and Medicine

|January 22, 2022

Summary

This study introduces a new method to precisely locate the heart using body surface potentials, improving electrocardiographic imaging (ECGI) accuracy. This geometric correction enhances clinical diagnosis of heart dysfunction.

Keywords:

Body surface potential mapping Electrocardiographic imaging Inverse problem Optimization

More Related Videos

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

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

Published on: January 8, 2013

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Related Experiment Videos

Last Updated: Oct 5, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

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

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

Published on: January 8, 2013

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

Biomedical Engineering
Cardiology
Computational Biology

Background:

Electrocardiographic imaging (ECGI) assesses heart bioelectricity for diagnosing cardiac dysfunction.
ECGI accuracy relies on precise heart localization within the torso, which is sensitive to positional changes.
Current ECGI methods are limited by geometric errors from variations in heart position due to respiration or body posture.

Purpose of the Study:

To develop and validate a novel method for reconstructing cardiac geometry using body surface potential measurements.
To address and reduce geometric errors in ECGI caused by heart position variability.
To improve the accuracy and clinical utility of ECGI in assessing cardiac electrical activity.

Main Methods:

A novel iterative approach was developed to simultaneously estimate cardiac position and bioelectric sources over multiple heartbeats.
The method utilizes noninvasively acquired body surface potential measurements.
Geometric correction was evaluated across diverse scenarios, including varying activation sequences, cardiac motion ranges, and electrode configurations.

Main Results:

The geometric correction method significantly reduces geometric error in ECGI reconstructions.
ECGI accuracy is demonstrably improved across a wide range of testing conditions.
Variability in ECGI solutions between heartbeats is substantially reduced after applying the geometric correction.

Conclusions:

The proposed geometric correction method enhances the accuracy of ECGI by accounting for heart position changes.
This technique improves the reliability and clinical applicability of noninvasive cardiac imaging.
Accurate cardiac geometry reconstruction is crucial for precise assessment of cardiac bioelectric activity.