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

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
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

117
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
117
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

872
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...
872
Dysrhythmias IV: Characteristics of Bradyarrhythmias01:18

Dysrhythmias IV: Characteristics of Bradyarrhythmias

118
Bradyarrhythmias are cardiac rhythm disorders characterized by a slower-than-normal heart rate, typically defined as fewer than 60 beats per minute. Some of which are discussed here:Sinus BradycardiaSinus bradycardia presents a heart rate lower than 60 beats per minute, with a regular rhythm originating from the SA node. The ECG typically shows normal P waves preceding each QRS complex, a normal PR interval (0.12 to 0.20 seconds), and a normal QRS duration (0.06 to 0.10 seconds).First-Degree AV...
118
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

1.2K
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
1.2K
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

23
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
23

You might also read

Related Articles

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

Sort by
Same author

Authors' response to commentary on "Echocardiographic phenotypes in sepsis: identifying subgroups using latent profile analysis".

Journal of intensive care·2026
Same author

Atrial cardiomyopathy as a multidomain disease: longitudinal evidence for autonomic remodelling.

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·2026
Same author

Cardio Heart Connect: Protocol for a Randomized Trial of a Commercially Available mHealth Fitness Intervention for Cardiac Rehabilitation After Transcatheter Aortic Valve Replacement.

medRxiv : the preprint server for health sciences·2026
Same author

Mechanistic interpretations of sustained ventricular fibrillation: The role of mapping methodology, model and time.

Heart international·2026
Same author

Mapping the plasma proteomic architecture of systemic lupus erythematosus.

JCI insight·2026
Same author

Peak QRS/T ratio and the spatial ventricular gradient differentiate acute vs chronic left bundle branch block after transcatheter aortic valve replacement.

Heart rhythm O2·2026

Related Experiment Video

Updated: Sep 10, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

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

Published on: April 11, 2025

903

Artificial Intelligence-Enhanced Electrocardiography for Complete Heart Block Risk Stratification.

Arunashis Sau1,2, Henry Zhang1, Joseph Barker1

  • 1National Heart and Lung Institute, Imperial College London, London, United Kingdom.

JAMA Cardiology
|August 20, 2025
PubMed
Summary

Artificial intelligence-enhanced electrocardiography (AI-ECG) can predict complete heart block (CHB) risk. This AI-ECG tool, AIRE-CHB, offers improved risk stratification compared to traditional methods for identifying patients at risk of CHB.

More Related Videos

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

8.7K
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

Related Experiment Videos

Last Updated: Sep 10, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

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

Published on: April 11, 2025

903
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

8.7K
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

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Complete heart block (CHB) is a serious condition with crude risk stratification using current electrocardiography (ECG).
  • Artificial intelligence-enhanced ECG (AI-ECG) shows promise in identifying subclinical diseases.
  • There is a need for improved methods to predict incident CHB.

Purpose of the Study:

  • To develop and validate an AI-ECG risk estimator for predicting incident CHB.
  • To assess the performance of the AI-ECG model against traditional risk factors.

Main Methods:

  • A cohort study involving development and external validation.
  • Utilized a residual convolutional neural network architecture with a discrete-time survival loss function.
  • Trained the AI-ECG model (AIRE-CHB) to predict new CHB diagnoses.

Main Results:

  • AIRE-CHB demonstrated strong predictive performance in both development (C-index 0.836, AUROC 0.889) and validation cohorts (C-index 0.936).
  • The AI-ECG model significantly outperformed traditional bifascicular block detection (AUROC 0.594).
  • High-risk individuals identified by AIRE-CHB had substantially increased hazard ratios for developing CHB.

Conclusions:

  • A novel deep learning model, AIRE-CHB, can effectively identify the risk of incident CHB.
  • AIRE-CHB has the potential to enhance clinical decision-making for patients with syncope or at risk of high-grade atrioventricular block.
  • This AI-ECG approach offers a significant advancement over current risk stratification methods for CHB.