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

878
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...
878
Electrocardiogram01:29

Electrocardiogram

3.2K
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...
3.2K
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

6.8K
The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
6.8K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.4K
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...
8.4K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

3.9K
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
3.9K

You might also read

Related Articles

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

Sort by
Same author

Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.

Sleep advances : a journal of the Sleep Research Society·2026
Same author

Artificial intelligence as a computational kit for digital biomarker discovery.

Journal of translational internal medicine·2026
Same author

Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.

Journal of medical Internet research·2026
Same author

Artificial Intelligence-Enhanced Electrocardiogram for Longitudinal Evaluation of Right Ventricular Dysfunction After Acute Pulmonary Embolism: Rational and Design of CURES-CARE Study.

Pulmonary circulation·2026
Same author

Smartphone Keystroke Biomarkers as Predictors of Adverse Neuropsychiatric Sequelae After Trauma in Trauma Survivors: Prospective Observational Cohort Study.

Journal of medical Internet research·2026
Same author

Microbial Dysbiosis in Photodermatoses: Formation, Pathogenesis and Intervention Strategies.

Current issues in molecular biology·2026

Related Experiment Video

Updated: Sep 12, 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

3.9K

An Electrocardiogram Foundation Model Built on over 10 Million Recordings.

Jun Li1,2,3, Aaron D Aguirre4,5, Valdery Moura5,6

  • 1National Institute of Health Data Science, Peking University, Beijing.

NEJM AI
|August 7, 2025
PubMed
Summary

A new artificial intelligence (AI) foundation model, ECGFounder, analyzes electrocardiograms (ECGs) for cardiovascular disease diagnosis. It achieves expert-level performance, even on single-lead ECGs, advancing remote patient monitoring.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
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

2.2K

Related Experiment Videos

Last Updated: Sep 12, 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

3.9K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
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

2.2K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Artificial intelligence (AI) shows promise in electrocardiogram (ECG) analysis for cardiovascular disease.
  • Foundation models enhance medical AI, improving diagnosis and knowledge transfer.
  • Challenges in ECG AI include limited data and poor generalization, especially for single-lead ECGs.

Purpose of the Study:

  • To develop a general-purpose ECG foundation model (ECGFounder) for comprehensive cardiovascular disease diagnosis.
  • To create a model that is effective out-of-the-box and easily fine-tunable for various downstream tasks.
  • To extend ECG analysis capabilities to reduced-lead ECGs, including single-lead ECGs for mobile and remote monitoring.

Main Methods:

  • Developed ECGFounder using 10,771,552 ECGs from 1,818,247 subjects with 150 diagnostic labels.
  • Leveraged real-world ECG annotations from cardiologists for broad diagnostic capabilities.
  • Extended model applicability to single-lead ECGs for diverse applications.

Main Results:

  • ECGFounder achieved expert-level performance (AUROC > 0.95 for 80 diagnoses) on internal validation.
  • Demonstrated strong classification and generalization across diagnoses on external validation sets.
  • Outperformed baseline models by 3-5 AUROC points in demographic analysis, event detection, and cross-modality diagnosis after fine-tuning.

Conclusions:

  • The ECG foundation model effectively generalizes across tasks, enhancing cardiovascular diagnostics.
  • Facilitates integration with cloud systems for wearable ECG data analysis.
  • Significantly advances AI in cardiology and aids in cardiac condition management.