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

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

14.9K
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...
14.9K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.9K
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...
1.9K
Electrocardiogram01:29

Electrocardiogram

7.8K
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...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework.

Npj imaging..·2025
Same author

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Computers in biology and medicine·2025
Same author

A wavelet subband based LSTM model for 12-lead ECG synthesis from reduced lead set.

Biomedical engineering letters·2024
Same author

Atrial Fibrillation Burden Estimation Using Multi-Task Deep Convolutional Neural Network.

IEEE journal of biomedical and health informatics·2022
Same author

Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

IEEE journal of biomedical and health informatics·2022
Same author

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification.

IEEE journal of biomedical and health informatics·2021
Same journal

Interpretable Model for Clinical Use in Left Atrial Appendage Segmentation via an Optimised Deformable-Attention U-Net With Spatial-Channel Fusion.

Healthcare technology letters·2026
Same journal

Driving Innovation: Transatlantic Attitudes to the <i>Bionics Bus</i> as a Vehicle for Health Transformation and STEM Engagement.

Healthcare technology letters·2026
Same journal

Gamified Digital Solutions for Tinnitus Health Literacy: The Erasmus+ Project TinWise.

Healthcare technology letters·2026
Same journal

Effect of Technology-Supported Measures Used for Care Transition Decisions for Chronic Disease Patients: A Systematic Review and Meta-Analysis.

Healthcare technology letters·2026
Same journal

Bibliometric Trends in the Integration of Computer Vision With Healthcare.

Healthcare technology letters·2026
Same journal

Parameter-Efficient Deep Learning Models for Vital Sign Estimation From PPG.

Healthcare technology letters·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.1K

Exploiting multi-lead electrocardiogram correlations using robust third-order tensor decomposition.

Sibasankar Padhy1, Samarendra Dandapat1

  • 1Department of Electronics and Electrical Engineering , Indian Institute of Technology Guwahati , Guwahati PIN-781 039 , Assam , India.

Healthcare Technology Letters
|November 27, 2015
PubMed
Summary
This summary is machine-generated.

A novel tensor decomposition method significantly reduces multi-lead electrocardiogram (MECG) data size by over 45 times. This robust technique preserves diagnostic accuracy while enabling efficient storage and analysis of vital ECG information.

Keywords:
ECG wave segmentationMECGP-wavePTB diagnostic databaseQRS-complexST-segmentT-wavecompression ratiodiagnostic distortion leveldimension reductionelectrocardiographyencodinghigh-frequency noisehigher-order singular value decompositioninter-beat beatsinter-leadintra-beat beatsmedical signal processingmultilead electrocardiogram correlationsmultiscale analysisnoise removalorder-3 tensor structurerobust third-order tensor decompositionsignal denoisingsingular value decompositionstorage datasuccessive beats

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

6.1K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.3K

Related Experiment Videos

Last Updated: Mar 29, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.1K
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

6.1K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.3K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Science

Background:

  • Multi-lead electrocardiogram (MECG) data requires substantial storage, posing challenges for efficient analysis and transmission.
  • Existing data reduction methods for MECG may compromise diagnostic integrity.

Purpose of the Study:

  • To introduce a robust third-order tensor decomposition technique for dimensionality reduction of 12-lead MECG data.
  • To enhance the efficiency of MECG data storage and processing while maintaining diagnostic quality.

Main Methods:

  • Representing MECG data using a 3D tensor structure (leads, beats, samples).
  • Applying higher-order singular value decomposition (HOSVD) for tensor decomposition.
  • Integrating multiscale analysis for ECG characteristic wave segmentation and noise reduction.

Main Results:

  • Achieved significant data volume reduction of MECG data by over 45 times.
  • Demonstrated superior performance compared to existing algorithms, with compression ratios under 10.
  • Maintained acceptable diagnostic distortion levels post-compression.

Conclusions:

  • The proposed third-order tensor decomposition with multiscale analysis offers an effective solution for MECG data compression.
  • This method provides a balance between high compression ratios and diagnostic fidelity.
  • The technique holds promise for improving the efficiency of clinical ECG data management and remote monitoring.