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

Classification of Skeletal Muscle Fibers

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

You might also read

Related Articles

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

Sort by

Same author

Plasticity of source-sink dynamics contributes to wheat yield stability.

Nature communications·2026

Same author

Examining Motor Unit Properties of Upper Limb Muscles Near the Zone of Injury in Chronic Cervical Spinal Cord Injury.

Journal of neurotrauma·2026

Same author

MSTO1-related mitochondrial myopathy and ataxia syndrome: Case series and literature review.

Neuromuscular disorders : NMD·2026

Same author

Cultivars and variety mixtures in small grain cereals: To mix or not to mix?

Molecular plant·2025

Same author

Algorithm for jitter measurement in neuromuscular junction disease.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2025

Same author

Systemic Neisseria sicca Infection Associated With Ravulizumab Therapy in Myasthenia Gravis.

Muscle & nerve·2025

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

Transparent muscle characterization using quantitative electromyography: different binarization mappings.

Meena AbdelMaseeh, Tsu-Wei Chen, Pascal Poupart

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|April 25, 2014

Summary

This summary is machine-generated.

Quantitative electromyography (QEMG) improves muscle disorder diagnosis by using binarization mappings to enhance transparent muscle characterization from motor unit potential trains (MUPTs). This method offers more accurate and objective muscle assessments than traditional subjective evaluations.

More Related Videos

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Performing In Vivo and Ex Vivo Electrical Impedance Myography in Rodents

Performing In Vivo and Ex Vivo Electrical Impedance Myography in Rodents

Published on: June 8, 2022

Related Experiment Videos

Last Updated: Apr 30, 2026

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Performing In Vivo and Ex Vivo Electrical Impedance Myography in Rodents

Performing In Vivo and Ex Vivo Electrical Impedance Myography in Rodents

Published on: June 8, 2022

Area of Science:

Biomedical Engineering
Neurology
Signal Processing

Background:

Traditional electromyography (EMG) assessment of neuromuscular disorders relies on subjective visual and auditory analysis of signals.
This subjectivity leads to variability in muscle characterization, dependent on examiner expertise.
Quantitative electromyography (QEMG) offers a more objective approach by analyzing motor unit potential trains (MUPTs).

Purpose of the Study:

To enhance the transparency and accuracy of muscle characterization using QEMG techniques.
To investigate the efficacy of binarization mappings in improving muscle categorization.
To identify optimal binarization mappings balancing accuracy and transparency.

Main Methods:

Extraction of motor unit potential trains (MUPTs) from needle-detected EMG signals using QEMG.
Estimation of features capturing motor unit potential (MUP) morphology and consistency within MUPTs.
Application and evaluation of 10 different binarization mappings on these features.
Comparison of transparent characterization methods with Gaussian mixture models (GMM) based approaches.

Main Results:

Four out of ten investigated binarization mappings, applied to transparent characterization methods, surpassed GMM-based methods in muscle categorization accuracy.
The use of appropriate binarization mappings mitigated accuracy loss typically associated with feature quantization.
Performance gains were linked to the utilization of more relevant features and optimized quantization for binary characterization.

Conclusions:

Binarization mappings can significantly improve the accuracy of transparent muscle characterization in QEMG.
This approach offers a more objective and reliable method for diagnosing neuromuscular disorders.
The findings suggest a pathway for more standardized and reproducible EMG-based diagnostics.