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

Motor unit potential characterization using "pattern discovery".

L J Pino1, D W Stashuk, S G Boe

  • 1Systems Design Engineering, University of Waterloo, Canada. ljpino@uwaterloo.ca

Medical Engineering & Physics
|August 19, 2007
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

Long-Term Endurance and Power Training May Facilitate Motor Unit Size Expansion to Compensate for Declining Motor Unit Numbers in Older Age.

Frontiers in physiology·2019
Same author

The Effect of High Dose Vitamin D3 on Physical Performance in Frail Older Adults. A Feasibility Study.

The Journal of frailty & aging·2018
Same author

Failure to expand the motor unit size to compensate for declining motor unit numbers distinguishes sarcopenic from non-sarcopenic older men.

The Journal of physiology·2018
Same author

The reliability of methods to estimate the number and size of human motor units and their use with large limb muscles.

European journal of applied physiology·2018
Same author

The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

Medical & biological engineering & computing·2017
Same author

Nitric oxide production in normal, endotoxemic and critically ill colic horses.

Veterinary anaesthesia and analgesia·2017

Pattern discovery (PD) offers a transparent method for analyzing electromyographic (EMG) signals. This quantitative approach shows comparable accuracy to other classifiers in identifying neuromuscular disorders from motor unit potentials (MUPs).

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Clinical electromyography (EMG) relies on qualitative analysis of EMG signals to diagnose neuromuscular disorders.
  • Quantitative EMG methods offer improved accuracy but require complex statistical interpretation.
  • There is a need for more transparent and interpretable quantitative methods for EMG analysis.

Purpose of the Study:

  • To compare the accuracy of pattern discovery (PD) for characterizing motor unit potentials (MUPs) against common medical classifiers.
  • To demonstrate the transparency of the PD characterization method.
  • To evaluate the utility of PD in transforming EMG data into clinically relevant knowledge.

Main Methods:

  • Utilized clinical MUP data from normal subjects and patients with neuropathic disorders.

Related Experiment Videos

  • Employed pattern discovery (PD) for quantitative interpretation of EMG statistics.
  • Compared PD accuracy with Naïve Bayes, Decision Tree, and discriminant analysis classifiers.
  • Validated findings using simulated EMG data.

    • Main Results:

    • PD achieved an error rate of 30.3% on clinical data, comparable to Naïve Bayes (29.8%), Decision Tree (30.1%), and discriminant analysis (29%).
    • Similar performance was observed with simulated EMG data.
    • PD successfully interpreted MUP information, generating knowledge consistent with existing literature.

    Conclusions:

    • Pattern discovery provides a transparent and quantitative method for analyzing MUPs in EMG.
    • PD demonstrates potential for capturing and expressing clinically useful knowledge from electrophysiological data.
    • The transparency of PD, stemming from observable patterns, enhances its clinical applicability.