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Related Experiment Videos

Single channel surface electromyogram deconvolution to explore motor unit discharges.

Luca Mesin1

  • 1Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy. luca.mesin@polito.it.

Medical & Biological Engineering & Computing
|July 28, 2019
PubMed
Summary
This summary is machine-generated.

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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.
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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.
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This study presents a deconvolution technique to estimate motor unit (MU) firings from surface electromyogram (EMG) signals. The method accurately captures MU firing rates and synchronization, even at high force levels, using a single EMG channel.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Surface electromyogram (EMG) signals contain information about active motor units (MUs).
  • Estimating MU firing properties from EMG is challenging due to asynchronous summation of discharges.
  • Existing methods struggle to accurately quantify MU behavior, especially at higher force levels.

Purpose of the Study:

  • To introduce a novel deconvolution technique for estimating cumulative motor unit (MU) firings from surface EMG.
  • To validate the method's accuracy in simulations and experimental data.
  • To enable simpler and more accessible analysis of MU activity.

Main Methods:

  • A deconvolution algorithm was developed to estimate cumulative MU firings from single-channel EMG.
Keywords:
Iterative reweighted least squaresL1 optimizationMotor unit firing rateMotor unit synchronizationSurface EMG

Related Experiment Videos

  • Simulations were used to test the method's performance under varying MU firing rates and synchronization levels.
  • The technique was applied to experimental EMG data for validation.

    • Main Results:

    • The power spectral density of estimated MU firings reveals a low-frequency peak corresponding to the mean MU firing rate.
    • Simulated MU synchronization increased the amplitude and shifted the centroid of this peak to higher frequencies.
    • The method achieved over 90% correlation with simulated cumulative firings up to 150 Hz across all force levels.

    Conclusions:

    • The deconvolution technique accurately estimates MU cumulative firings and firing rates from surface EMG.
    • The method is sensitive to MU synchronization and effective even at high force levels.
    • Its single-channel requirement makes it feasible for various applications, including fatigue and pathology studies.