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

You might also read

Related Articles

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

Sort by

Same author

Testing the biobehavioral regulation of negative emotion as a mechanism of change in transdiagnostic youth psychotherapy: study protocol for a randomized controlled trial.

Trials·2026

Same author

Geographical Disparities in Colorectal Cancer in Canada: A Review.

Current oncology reports·2024

Same author

Geographical Disparities in Lung Cancer in Canada: A Review.

Current oncology reports·2024

Same author

Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2023

Same author

Detecting subject-specific fatigue-related changes in lifting kinematics using a machine learning approach.

Ergonomics·2022

Same author

Machine Learning in Modeling of Mouse Behavior.

Frontiers in neuroscience·2021

Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026

Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026

Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026

Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026

Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026

Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 29, 2026

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

Characterizing EMG data using machine-learning tools.

Jamileh Yousefi¹, Andrew Hamilton-Wright²

¹School of Computer Science, University of Guelph, Guelph, ON, Canada.

Computers in Biology and Medicine

|May 27, 2014

Summary

This summary is machine-generated.

Accurate electromyographic (EMG) signal characterization using machine learning is vital for diagnosing neuromuscular disorders. This review examines various classification methods for EMG analysis, highlighting advancements in neuromuscular pathology detection.

Keywords:

Classification EMG characterization EMG electromyography Machine learning Myopathy Neuromuscular disease Neuropathy

More Related Videos

A Real-Time Wearable Electromyography Measurement System for Small Animals

A Real-Time Wearable Electromyography Measurement System for Small Animals

Published on: November 15, 2024

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Related Experiment Videos

Last Updated: Apr 29, 2026

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

A Real-Time Wearable Electromyography Measurement System for Small Animals

A Real-Time Wearable Electromyography Measurement System for Small Animals

Published on: November 15, 2024

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Area of Science:

Biomedical Engineering
Neurology
Data Science

Background:

Electromyographic (EMG) signal characterization is crucial for diagnosing neuromuscular disorders.
Machine learning pattern classification algorithms are widely employed for EMG analysis.
Developing accurate and computationally efficient EMG characterization strategies is an ongoing challenge.

Purpose of the Study:

To critically review classification methodologies used in EMG characterization.
To present the state-of-the-art accomplishments in EMG signal analysis for neuromuscular pathology.
To group and summarize findings of various classification techniques.

Main Methods:

Review of machine learning based pattern classification algorithms for EMG signal characterization.
Categorization of techniques by their underlying methodology.
Synthesis of salient findings for each method studied.

Main Results:

Identification of various classification methodologies applied to EMG signal characterization.
Summary of the performance and characteristics of different classifiers.
Highlighting advancements and current state in the field, particularly for neuromuscular pathology.

Conclusions:

Machine learning offers powerful tools for EMG signal characterization in neuromuscular disorders.
A comprehensive understanding of different classification methods is essential for accurate diagnosis.
Continued research in this area promises improved diagnostic capabilities for neurological conditions.