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

You might also read

Related Articles

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

Sort by
Same author

A continuous network physiology analysis of brain-heart interactions in epileptic seizures.

Scientific reports·2026
Same author

Toward future diagnostics of Parkinson's disease: a perspective on multimodal motor assessment and personalized digital twins.

Frontiers in aging neuroscience·2026
Same author

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

Computers in biology and medicine·2026
Same author

Translational perspectives on brain-heart interplay: From methodologies to clinical applications.

Computers in biology and medicine·2026
Same author

Collection of Ambulatory Electrocardiogram and Behavioral Data for the Identification of Digital Biomarkers for Heart Failure (CATCH-ECG): Protocol for a Prospective Cohort Study.

JMIR research protocols·2025
Same author

Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation.

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: Mar 27, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.4K

ECG De-noising: A comparison between EEMD-BLMS and DWT-NN algorithms.

Kevin Kærgaard, Søren Hjøllund Jensen, Sadasivan Puthusserypady

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    This study compares two artifact removal techniques for electrocardiogram (ECG) signals. The Discrete Wavelet Transform-Neural Network (DWT-NN) method showed superior performance in minimizing noise, especially for complex artifact types.

    More Related Videos

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    34.5K
    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

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
    08:08

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

    Published on: May 10, 2017

    15.4K
    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    34.5K
    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

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Cardiology

    Background:

    • Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions.
    • Artifacts frequently contaminate ECG recordings, hindering accurate analysis.
    • Effective artifact minimization is essential for reliable ECG interpretation.

    Purpose of the Study:

    • To propose and compare two adaptive artifact minimization techniques for ECG signals: EEMD-BLMS and DWT-NN.
    • To evaluate the performance of these methods on simulated and real ECG data.
    • To determine the most effective method for different types of noise interference.

    Main Methods:

    • Ensemble Empirical Mode Decomposition with Block Least Mean Square (EEMD-BLMS) algorithm.
    • Discrete Wavelet Transform followed by a Neural Network (DWT-NN).
    • Testing on simulated ECG signals (Type-I and Type-II noise) and real ECG recordings.

    Main Results:

    • Both EEMD-BLMS and DWT-NN performed effectively on Type-I simulated signals (noise outside ECG band).
    • DWT-NN demonstrated superior performance on Type-II simulated signals (noise inside and outside ECG band).
    • On real ECG data, DWT-NN showed a slight advantage in reducing high-frequency artifacts.

    Conclusions:

    • The DWT-NN method is more effective for minimizing complex artifacts in ECG signals.
    • Both adaptive techniques offer viable solutions for ECG artifact reduction.
    • Further research may explore hybrid approaches for enhanced ECG signal quality.