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

6.9K
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...
6.9K
Instrumentation Amplifier01:25

Instrumentation Amplifier

1.1K
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
1.1K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.5K
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...
13.5K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

A novel sulfur-containing photoinitiator based on benzophenone derivatives for rapid photopolymerization.

Smart molecules : open access·2026
Same author

Tetrahedral DNA nano-PROTACs enable enhanced ocular penetration and efficient nucleolin degradation for choroidal neovascularization therapy.

Signal transduction and targeted therapy·2026
Same author

Fraxetin Inhibits UGT1A1 and UGT1A9 Activities In Vitro: Inhibition Kinetics, Molecular Dynamics Simulation, and Prediction of Herb-Drug Interaction Risk.

Pharmaceuticals (Basel, Switzerland)·2026
Same author

Life-span-dependent transcriptional dynamics of the human heart.

Science advances·2026
Same author

The potential of TRPC channel-mediated autophagy in myocardial ischemia-reperfusion injury.

Journal of cardiothoracic surgery·2026
Same author

Persisting predominant clinical CC87 <i>Listeria monocytogenes</i> in China: evolutionary dynamics and diversification.

Emerging microbes & infections·2026

Related Experiment Video

Updated: Feb 26, 2026

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

9.1K

EQA-MDL: Wearable ECG Signal Quality Assessment via Multi-scale Difference Learning.

Haoyi Fan, Jiawei Luo, Huihui Chang

    IEEE Journal of Biomedical and Health Informatics
    |February 24, 2026
    PubMed
    Summary

    This study introduces EQA-MDL, a novel self-supervised method for electrocardiogram (ECG) signal quality assessment. It accurately identifies noisy ECG segments without labeled data, improving wearable device data reliability.

    More Related Videos

    Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
    04:45

    Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

    Published on: May 5, 2022

    3.0K
    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
    10:03

    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

    Published on: July 22, 2022

    5.1K

    Related Experiment Videos

    Last Updated: Feb 26, 2026

    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

    9.1K
    Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
    04:45

    Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

    Published on: May 5, 2022

    3.0K
    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
    10:03

    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

    Published on: July 22, 2022

    5.1K

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Machine Learning

    Background:

    • Signal quality assessment (SQA) is vital for wearable device data accuracy.
    • Existing electrocardiogram (ECG) quality assessment methods often require labeled data and struggle with precise noisy area localization.

    Purpose of the Study:

    • To develop a self-supervised method for accurate ECG signal quality assessment and noisy segment localization.
    • To overcome limitations of existing methods relying on labeled data.

    Main Methods:

    • Proposed EQA-MDL method using multi-scale difference learning.
    • Incorporated a noise generation module for pseudo-anomaly sample creation.
    • Combined a reconstruction framework with multi-scale difference learning for feature extraction.

    Main Results:

    • EQA-MDL demonstrated superior performance in identifying noisy ECG segments.
    • The method significantly outperformed state-of-the-art approaches across multiple datasets.
    • Achieved accurate anomaly localization without relying on pre-labeled data.

    Conclusions:

    • EQA-MDL offers a robust and effective self-supervised solution for ECG signal quality assessment.
    • The proposed approach enhances the reliability of data collected from wearable devices.
    • This method advances the field of automated ECG analysis and noise detection.