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 Experiment Videos

Automatic assessment of electromyogram quality

C Sinderby1, L Lindström, A E Grassino

  • 1Department of Medicine, Notre Dame Hospital, University of Montreal, Quebec, Canada.

Journal of Applied Physiology (Bethesda, Md. : 1985)
|November 1, 1995
PubMed
Summary

New computer algorithms automatically detect and quantify artifacts in diaphragm electromyogram (EMGdi) signals. This frequency domain analysis provides a reliable method for cleaning EMGdi data, improving accuracy in research and clinical settings.

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

Impact of fetal and maternal characteristics on the accuracy and precision of sonographic fetal weight estimation: population-based study.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology·2025
Same author

Polycystic ovary syndrome and risk of stillbirth: a nationwide register-based study.

BJOG : an international journal of obstetrics and gynaecology·2021
Same author

The Dental Curriculum of Sweden.

The Dental register·2021
Same author

Propagule pressure increase and phylogenetic diversity decrease community's susceptibility to invasion.

BMC ecology·2017
Same author

Evolutionary constraints of warning signals: A genetic trade-off between the efficacy of larval and adult warning coloration can maintain variation in signal expression.

Evolution; international journal of organic evolution·2016
Same author

Population-based analysis of colorectal cancer risk after oophorectomy.

The British journal of surgery·2016

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Diaphragm electromyogram (EMGdi) power spectrum analysis is crucial for respiratory research but is time-consuming.
  • Objective criteria for quantifying EMGdi signal contamination are lacking, hindering reliable analysis.
  • Common artifacts like electrocardiogram (ECG) interference and noise can compromise EMGdi data integrity.

Purpose of the Study:

  • To develop and validate computer algorithms for automatic artifact detection and quantification in EMGdi signals.
  • To objectively assess signal quality and contamination in the frequency domain.
  • To establish a reliable and reproducible method for processing EMGdi data.

Main Methods:

  • Development of computer algorithms for automatic selection of artifact-free EMGdi signals.

Related Experiment Videos

  • Application of algorithms to human and canine EMGdi data, as well as computer-simulated power spectra.
  • Quantification of signal disturbances using derived indexes, including noise, electrode motion, esophageal peristalsis, and ECG activity.

    • Main Results:

    • Algorithms successfully identified and quantified various artifacts affecting EMGdi signals.
    • Indexes derived from algorithms demonstrated reliability comparable to or exceeding manual visual selection by experts.
    • Setting appropriate thresholds allowed for high signal acceptance with minimal artifact-induced fluctuations (10-15%) in EMGdi center frequency.

    Conclusions:

    • Frequency domain application of computer algorithms offers a reliable and reproducible means to objectively quantify sources contaminating EMGdi signals.
    • Automated artifact quantification enhances the efficiency and objectivity of EMGdi analysis.
    • This approach improves the integrity and interpretability of EMGdi data in physiological studies.