Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electrocardiogram

Electrocardiogram

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 the T...

Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

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...

Instrumentation Amplifier

Instrumentation Amplifier

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...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

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 to...

ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...

You might also read

Related Articles

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

Sort by

Same author

I-BEAT: Interpretable Transformer Model for Intra-Beat Wave Detection on Ambulatory ECG.

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

DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.

IEEE journal of biomedical and health informatics·2025

Same author

A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

IEEE transactions on bio-medical engineering·2024

Same author

Machine learning based detection of T-wave alternans in real ambulatory conditions.

Computer methods and programs in biomedicine·2024

Same author

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Medical & biological engineering & computing·2023

Same author

Machine Learning approach for TWA detection relying on ensemble data design.

Heliyon·2023

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

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

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

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

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

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

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

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

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

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

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

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

See all related articles

Search research articles

Related Experiment Video

Updated: May 25, 2026

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

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

Published on: April 11, 2025

Matrix completion based ECG compression.

Luisa F Polania¹, Rafael E Carrillo, Manuel Blanco-Velasco

¹Dept of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA. polania@eecis.udel.edu

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

|January 19, 2012

Summary

This summary is machine-generated.

This study introduces a novel electrocardiogram (ECG) compression algorithm using matrix completion. The method achieves high compression ratios and excellent reconstruction quality with low complexity.

Related Experiment Videos

Last Updated: May 25, 2026

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

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

Published on: April 11, 2025

Area of Science:

Biomedical Engineering
Signal Processing
Data Compression

Background:

Electrocardiogram (ECG) data requires efficient compression for storage and transmission.
Existing compression techniques face limitations in achieving high ratios while maintaining signal integrity.
Matrix completion offers a novel approach for signal recovery from limited data.

Purpose of the Study:

To develop and evaluate an innovative ECG compression algorithm based on matrix completion.
To assess the compression ratios and reconstruction quality of the proposed method.
To analyze the computational complexity and noise tolerance of the algorithm.

Main Methods:

The proposed algorithm utilizes matrix completion, a signal processing technique for recovering low-rank matrices.
ECG records are normalized to obtain a low-rank matrix.
The matrix completion technique recovers the ECG data matrix from a limited number of observed entries.

Main Results:

The algorithm achieves high compression ratios comparable to existing methods.
The method demonstrates good tolerance to quantization noise.
High-quality reconstruction of the ECG signals is achieved.

Conclusions:

Matrix completion provides an effective paradigm for ECG data compression.
The proposed algorithm offers a low-complexity solution with excellent performance.
This technique holds promise for efficient ECG data management.