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 Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

986
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...
986
Electrocardiogram01:29

Electrocardiogram

4.0K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
4.0K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

9.6K
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...
9.6K

You might also read

Related Articles

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

Sort by
Same author

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same author

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt.

... International Conference on Learning Representations·2026
Same author

Catheter Ablation for Persistent Atrial Fibrillation.

The New England journal of medicine·2026
Same author

Patients' Perspectives on Personalized Glycemic Targets for Coronary Artery Disease Prevention Based on the Haptoglobin Phenotype: A Qualitative Study.

CJC open·2026
Same author

Beyond the ICD: Navigating Ventricular Tachycardia Suppression Strategies in the Modern Era.

Current cardiology reports·2026
Same author

Uric acid promotes dietary lipid absorption through microbiome and metabolomic remodeling via a liver-gut endocrine axis.

Cell host & microbe·2026

Related Experiment Video

Updated: Oct 22, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.0K

Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data.

Prashnna K Gyawali, Jaideep Vitthal Murkute, Maryam Toloubidokhti

    IEEE Transactions on Bio-Medical Engineering
    |August 30, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study demonstrates the feasibility of disentangling anatomical variations in electrocardiographic (ECG) data using deep generative models. This approach improves the accuracy of automated ECG analysis, particularly for localizing ventricular activation origins.

    More Related Videos

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.4K
    A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
    18:11

    A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

    Published on: December 28, 2012

    24.4K

    Related Experiment Videos

    Last Updated: Oct 22, 2025

    Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
    06:07

    Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

    Published on: May 23, 2021

    4.0K
    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.4K
    A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
    18:11

    A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

    Published on: December 28, 2012

    24.4K

    Area of Science:

    • Cardiology
    • Machine Learning
    • Biomedical Signal Processing

    Background:

    • Inter-subject anatomical variations pose a significant challenge in automated electrocardiographic (ECG) data analysis.
    • Existing methods often struggle to isolate these anatomical factors from task-relevant information.

    Purpose of the Study:

    • To investigate the potential of disentangled representation learning for separating inter-subject anatomical variations in ECG data.
    • To develop and evaluate a method for achieving disentanglement of anatomical factors in ECG signals.

    Main Methods:

    • Introduction of SimECG, a novel 12-lead ECG dataset procedurally generated with controlled anatomical factors.
    • Evaluation and comparison of deep generative models utilizing nonparametric Indian Buffet Process for latent density modeling.
    • Assessment of disentangling ability using a proposed disentanglement score.

    Main Results:

    • Demonstrated concrete evidence for disentangling key anatomical factors from task-relevant factors in ECG data.
    • Achieved a 92.1% disentanglement score, successfully separating five anatomical factors and one task-relevant factor.
    • Showcased significant improvements in the downstream task of localizing ventricular activation origin, with notable gains on both simulated and real ECG datasets compared to baseline models.

    Conclusions:

    • The study confirms the importance and feasibility of disentangled representation learning for ECG data.
    • Highlights a promising research direction for addressing inter-subject variations in automated ECG analysis.