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

Adaptive motor unit action potential clustering using shape and temporal information

D Stashuk1, Y Qu

  • 1Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.

Medical & Biological Engineering & Computing
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

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

[Baseline characteristics of the West China Bone Health Cohort and analysis on the influencing factors of osteoporosis and fragility fracture risk].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same author

Dietary inclusion of black soldier fly larvae as a partial protein source: effects on growth performance, carcass traits and meat quality of broilers.

British poultry science·2026
Same author

[A comparative study on perioperative outcomes and learning curves of domestic robot-assisted versus Da Vinci Xi robot-assisted partial nephrectomy].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2026
Same author

What has happened to river macroinvertebrate biodiversity in England and Wales over the past 30 years?

Journal of environmental management·2026
Same author

Anbenitamab in previously treated HER2-positive gastric cancer (KC-WISE): prespecified interim analysis of a randomized, phase III clinical trial.

Annals of oncology : official journal of the European Society for Medical Oncology·2026
Same author

[The overall study protocol for the West China Bone Health Cohort].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same journal

A novel SE-ResNet architecture for continuous estimation of wrist and hand movements from HD-sEMG.

Medical & biological engineering & computing·2026
Same journal

Anti-aliasing-enhanced WaveUNet for clinically reliable 12-lead ECG reconstruction from limited 3-lead input.

Medical & biological engineering & computing·2026
Same journal

Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation.

Medical & biological engineering & computing·2026
Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026
Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026
Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026
See all related articles

This study introduces an adaptive algorithm for decomposing electromyography (EMG) signals. It accurately groups motor unit action potentials (MUAPs) and forms motor unit action potential trains (MUAPTs) without prior knowledge.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Electromyography (EMG) signal decomposition is crucial for understanding motor control.
  • Accurate identification and grouping of motor unit action potentials (MUAPs) are challenging.
  • Existing methods often require prior knowledge of motor unit characteristics.

Purpose of the Study:

  • To develop an adaptive algorithm for grouping MUAPs and creating motor unit action potential trains (MUAPTs).
  • To enable data-driven clustering based on MUAP shape and firing patterns.
  • To provide accurate temporal parameter estimation for MUAPTs, even when incomplete.

Main Methods:

  • An adaptive clustering algorithm is employed to group detected MUAPs.
  • Data-driven criteria, including MUAP shape and firing patterns, are used for cluster formation.

Related Experiment Videos

  • A temporal parameter estimation algorithm is utilized for obtaining firing pattern information.

    • Main Results:

    • The algorithm successfully groups MUAPs and forms MUAPTs from composite EMG signals.
    • Clustering results are accurate and useful when applied to real concentric-needle EMG data.
    • The adaptive algorithm demonstrates more robust performance than classical leader-based methods.
    • It accurately estimates the number of motor units and generates more complete MUAPTs.

    Conclusions:

    • The developed adaptive algorithm offers a robust and accurate method for EMG signal decomposition.
    • It effectively identifies and groups MUAPs without requiring a priori information.
    • This approach enhances the accuracy of motor unit analysis and MUAPT reconstruction.