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...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Does maternal respiration modulate maternal-fetal cardiovascular coupling?

Computers in biology and medicine·2026

Same author

Intra-operative fetal monitoring during non-obstetric surgery for complications after bariatric surgery: a standard operating procedure.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2026

Same author

Study protocol for a multicenter phase II prospective externally controlled non-inferiority trial of hypofractionated re-irradiation in patients with recurrent high-grade glioma (RISinG).

PloS one·2026

Same author

Non-invasive electrophysiological monitoring vs conventional monitoring during labour in a tertiary obstetric care centre in the Netherlands: study protocol of a cohort intervention random sampling study (NIEM-II study).

BMJ open·2025

Same author

Incidence of radiation necrosis following different radiotherapy fractionation schedules for intracranial meningiomas.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]·2025

Same author

Uterine contraction frequency after initiation of labour epidural analgesia using electrohysterography monitoring: a prospective pilot study.

International journal of obstetric anesthesia·2025

Search research articles

Related Experiment Video

Updated: May 6, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Deep Learing for Sparse Domain Kalman Filtering With Applications on ECG Denoising and Motility Estimation.

I R de Vries, A M de Jong, M B van der Hout-van der Jagt

IEEE Transactions on Bio-Medical Engineering

|February 21, 2024

Summary

This summary is machine-generated.

This study introduces a novel Kalman-ISTA algorithm, merging sparse coding and Kalman filtering for efficient signal processing. The method excels in applications like electrocardiogram (ECG) denoising and object motility estimation.

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Related Experiment Videos

Last Updated: May 6, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Signal Processing
Machine Learning
Data Science

Background:

Sparse coding is a signal reconstruction technique widely used in signal processing.
Kalman filtering is a robust algorithm for estimating system states from noisy measurements.

Purpose of the Study:

To explore the combination of sparse coding and Kalman filtering for enhanced signal processing.
To demonstrate the potential of this combined approach in practical use-cases.

Main Methods:

The study extends the Iterative Shrinkage and Thresholding Algorithm (ISTA) by incorporating a Kalman filter within the sparse domain.
The proposed method can be implemented as a deep unfolded neural network.
It is applicable to signals with sparse representations and known relationships between consecutive measurements.

Main Results:

For electrocardiogram (ECG) denoising, the method achieved an 18.6 dB improvement in Signal-to-Noise Ratio (SNR), rivaling state-of-the-art.
In object motility estimation, a correlation of 0.84 with ground truth simulations was observed.

Conclusions:

The Kalman-ISTA algorithm offers advantages over standalone sparse coding or Kalman filtering.
Its low complexity and high generalizability facilitate easy adaptation for specific contexts or new applications.