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 Concept Videos

Electric Potential and Potential Difference01:16

Electric Potential and Potential Difference

5.6K
Suppose a positive test charge moves away from a positive static charge, then the Coulomb force does positive work, and its electric potential energy decreases. The potential energy per unit charge is defined as the electric potential. The electric potential is independent of the test charge.
When a test charge moves from the initial to the final position, the electric potential difference between those positions is defined as the ratio of the change in the potential energy to the charge on the...
5.6K
Motor Units00:46

Motor Units

61.7K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
61.7K
Motor Units01:13

Motor Units

7.5K
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
7.5K
Motor Unit Stimulation01:20

Motor Unit Stimulation

3.6K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
3.6K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

6.4K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
6.4K
Spontaneity02:21

Spontaneity

28.8K
A spontaneous process is one that occurs naturally under certain conditions. A nonspontaneous process, on the other hand, will not take place unless it is “driven” by the continual input of energy from an external source. Processes have a natural tendency to occur in one direction under a given set of conditions. Water will naturally flow downhill (spontaneous process), but uphill flow (nonspontaneous process) requires outside intervention such as the use of a pump. Iron exposed to...
28.8K

You might also read

Related Articles

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

Sort by
Same author

Electrodiagnostic Findings in COVID-19 ICU Survivors.

Cureus·2026
Same author

Reference values for near fiber EMG for the diagnosis of neuromuscular disorders.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2026
Same author

Peaks or threshold-crossing: A comparative analysis of neuromuscular jitter measurement methods.

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

Towards early diagnosis of amyotrophic lateral sclerosis using near fibre EMG.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2025
Same author

Neuromuscular junction instability with inactivity: morphological and functional changes after 10 days of bed rest in older adults.

The Journal of physiology·2025
Same author

Evaluating motor unit properties after nerve transfer surgery.

Journal of the neurological sciences·2025

Related Experiment Video

Updated: Jan 20, 2026

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles
09:07

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

Published on: September 25, 2015

22.1K

Average proportional consecutive interval difference accurately differentiates spontaneous activity from motor unit

Gregory T Robbins1, Bradley G Tucker1, Daniel W Stashuk2

  • 1Department of Physical Medicine and Rehabilitation, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania.

Muscle & Nerve
|August 24, 2019
PubMed
Summary

A new method using average proportional consecutive interval difference accurately distinguishes spontaneous activity (SA) from motor unit potentials (MUPs) in needle electromyography (EMG). This quantitative approach is valuable for future SA prevalence studies.

Keywords:
APCIDMCDelectromyographyfibrillationsirregularmotor unit potentialspositive sharp wavesregularityspontaneous activity

More Related Videos

Determination of the Spontaneous Locomotor Activity in Drosophila melanogaster
08:06

Determination of the Spontaneous Locomotor Activity in Drosophila melanogaster

Published on: April 10, 2014

13.2K
fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

7.6K

Related Experiment Videos

Last Updated: Jan 20, 2026

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles
09:07

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

Published on: September 25, 2015

22.1K
Determination of the Spontaneous Locomotor Activity in Drosophila melanogaster
08:06

Determination of the Spontaneous Locomotor Activity in Drosophila melanogaster

Published on: April 10, 2014

13.2K
fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

7.6K

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Electrophysiology

Background:

  • Accurate detection of spontaneous activity (SA) in needle electromyography (EMG) is crucial for prevalence studies.
  • Distinguishing SA from motor unit potentials (MUPs) is challenging due to morphological similarities.
  • Quantifying firing regularity for SA identification has lacked modern interface methods.

Purpose of the Study:

  • To develop and validate an objective method for differentiating SA from MUPs using quantitative EMG analysis.
  • To assess the utility of firing regularity metrics for SA detection in clinical electrodiagnostic evaluations.

Main Methods:

  • Prospective EMG recordings from patients undergoing electrodiagnostic evaluation were analyzed.
  • Decomposition-based quantitative EMG (DQEMG) software was customized to calculate descriptive statistics.
  • Average proportional consecutive interval difference, standard deviation (SD), and mean consecutive differences were computed.

Main Results:

  • The average proportional consecutive interval difference demonstrated high accuracy in differentiating SA from MUPs, with 97.5% sensitivity and 100.0% specificity.
  • One hundred and one of 124 recordings were successfully analyzed using DQEMG.
  • While average proportional consecutive interval difference was highly effective, SD and mean consecutive differences showed substantial overlap between SA and MUPs.

Conclusions:

  • Average proportional consecutive interval difference is a reliable quantitative metric for distinguishing SA from MUPs in needle EMG.
  • This method holds significant potential for improving the accuracy and efficiency of SA prevalence studies.