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

474
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...
474
Electrocardiogram01:29

Electrocardiogram

2.0K
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...
2.0K
Vision01:24

Vision

52.9K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.9K

You might also read

Related Articles

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

Sort by
Same author

Honeybee-DR: dynamic dependency management and lightweight reliability for mobile crowd computing.

Scientific reports·2026
Same author

Domain Adaptation for IMU Data to Enhance Objective Assessment of Friedreich Ataxia.

IEEE journal of biomedical and health informatics·2026
Same author

Enhancing the Objective Assessment of Friedreich Ataxia Severity: A Multiview IMU-Based Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Objective Assessment of Friedreich Ataxia in Children: Accounting for Developmental Deficits.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Reliable Objective Assessment of Friedreich Ataxia Through Isolation Forest-Based Anomaly Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Machine Learning Approach for Quantifying Hereditary Cerebellar Ataxia Severity and Evaluating Rehabilitation Effectiveness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: May 24, 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

350

Harnessing Vision Transformer Insights for Advanced Electrocardiogram Classification.

Pubudu L Indrasiri, Bipasha Kashyap, Pubudu N Pathirana

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces a novel Vision Transformer model for classifying electrocardiogram (ECG) data using wearable sensors. The approach enhances cardiac monitoring accessibility and efficiency, particularly in resource-limited settings.

    More Related Videos

    Optocardiography and Electrophysiology Studies of Ex Vivo Langendorff-perfused Hearts
    09:52

    Optocardiography and Electrophysiology Studies of Ex Vivo Langendorff-perfused Hearts

    Published on: November 7, 2019

    12.8K
    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    1.7K

    Related Experiment Videos

    Last Updated: May 24, 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

    350
    Optocardiography and Electrophysiology Studies of Ex Vivo Langendorff-perfused Hearts
    09:52

    Optocardiography and Electrophysiology Studies of Ex Vivo Langendorff-perfused Hearts

    Published on: November 7, 2019

    12.8K
    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    1.7K

    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Precise electrocardiogram (ECG) analysis is crucial for cardiac health monitoring.
    • Traditional bedside systems face cost and accessibility limitations.
    • Convolutional Neural Networks (CNNs) struggle with global context in ECG data due to fixed kernels.

    Purpose of the Study:

    • To develop an advanced ECG data classification method using wearable sensors and Vision Transformers.
    • To overcome limitations of traditional methods and CNNs in capturing global ECG signal context.

    Main Methods:

    • Proposed a Vision Transformer architecture for ECG data classification.
    • Transformed 1D ECG signals into 3-channel images using Markov Transition Fields (MTF), Recurrence Plots (RP), and Gramian Angular Fields (GAF).
    • Evaluated the model on the comprehensive ECG5000 dataset.

    Main Results:

    • The Vision Transformer model demonstrated superior performance in ECG data classification.
    • Achieved state-of-the-art results across various performance metrics.
    • Outperformed existing methods in classifying ECG data.

    Conclusions:

    • This approach offers a significant advancement in medical diagnostics for cardiac health.
    • Promises more accessible and efficient healthcare solutions, especially in resource-constrained environments.
    • Highlights the potential of Vision Transformers for analyzing complex biomedical signals.