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

619
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...
619
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

10
Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
10
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

25
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
25
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

41
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...
41
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

6.2K
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...
6.2K
Cardiomyopathy I: Introduction and Classification01:25

Cardiomyopathy I: Introduction and Classification

14
Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
14

You might also read

Related Articles

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

Sort by
Same author

SLLD: Single-lead ECG LQTS detection framework based on knowledge distillation.

Computer methods and programs in biomedicine·2026
Same author

Conceptual construction and scale development of leadership taking charge behavior in the Chinese cultural context.

Frontiers in psychology·2025
Same author

DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

A Dual-Scale Lead-Separated Transformer for ECG Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning.

Sensors (Basel, Switzerland)·2023
Same author

SEpi-3D: soft epipolar 3D shape measurement with an event camera for multipath elimination.

Optics express·2023

Related Experiment Video

Updated: Jul 14, 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

659

MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring.

Yuzhen Qin1, Li Sun2, Hui Chen2

  • 1Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China.

Computers in Biology and Medicine
|October 8, 2023
PubMed
Summary

This study enhances single-lead electrocardiogram (ECG) diagnostics for multiple cardiovascular diseases. A novel Multi-View Knowledge Transferring of ECG (MVKT-ECG) method effectively transfers knowledge from 12-lead ECG to single-lead models, improving accuracy.

Keywords:
Arrhythmia classificationDeep learningKnowledge transferNeural networkSingle-lead Electrocardiogram

More Related Videos

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

3.7K
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

Related Experiment Videos

Last Updated: Jul 14, 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

659
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

3.7K
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

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Electrocardiogram (ECG) is crucial for cardiovascular disease diagnosis.
  • Smart ECG devices increase demand for intelligent single-lead diagnostic systems.
  • Single-lead ECG interpretation for multi-label diagnosis faces challenges due to limited information.

Purpose of the Study:

  • To improve the diagnostic capabilities of single-lead ECG for multi-label disease classification.
  • To develop a teacher-student framework for knowledge transfer from multi-lead to single-lead ECG models.
  • To enhance the extraction of intricate details from single-lead ECG signals for better disease identification.

Main Methods:

  • Introduced a novel disease-aware Contrastive Lead-information Transferring (CLT) method.
  • Modified traditional Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD).
  • Implemented an inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG) strategy for comprehensive knowledge transfer.

Main Results:

  • MVKT-ECG significantly improved diagnostic performance for single-lead ECG.
  • The student model (single-lead) outperformed its baseline on public datasets (PTB-XL and ICBEB2018).
  • Performance gains were observed as 1.3-1.4% on PTB-XL and 3.2% on ICBEB2018.

Conclusions:

  • The MVKT-ECG strategy effectively transfers knowledge from multi-lead to single-lead ECG interpretation models.
  • This approach enhances the diagnostic accuracy of single-lead ECG for multi-label cardiovascular disease classification.
  • The proposed method offers a promising solution for intelligent single-lead ECG diagnostic systems.