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Are leg electromyogram profiles symmetrical?

S E Pierotti1, R A Brand, R H Gabel

  • 1Department of Orthopaedic Surgery, College of Medicine, University of Iowa, Iowa City 55242.

Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society
|September 1, 1991
PubMed
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Symmetry in electromyographic (EMG) patterns between limbs is common, but variations exist. Caution is advised when interpreting EMG abnormalities, considering speed and statistical limits for accurate assessment.

Area of Science:

  • Neuromuscular System Function
  • Biomechanical Analysis
  • Motor Control

Background:

  • Electromyographic (EMG) patterns are key indicators of neuromuscular system function.
  • Assessing abnormality often relies on comparing EMG patterns between contralateral limbs.
  • Establishing normal symmetry ranges is crucial for accurate interpretation.

Purpose of the Study:

  • To investigate the degree of symmetry in EMG patterns of homologous knee muscles during different gaits.
  • To determine if gait speed influences EMG pattern symmetry and repeatability.
  • To provide insights into the criteria for identifying abnormal EMG patterns.

Main Methods:

  • EMG signals from six homologous knee muscles were recorded during slow, fast, and self-selected gaits.

Related Experiment Videos

  • Linear envelopes of EMG signals were generated and ensemble-averaged across multiple gait cycles.
  • Transformed correlation coefficients and variance ratios were used to quantify symmetry between limbs.

    • Main Results:

    • Most homologous muscles exhibited a high degree of symmetry (mean r' = 0.797-0.953).
    • Occasional asymmetries were observed, including differences in signal phase (monophasic vs. biphasic).
    • Increasing gait speed generally improved signal repeatability, though pattern types could still vary.

    Conclusions:

    • Presuming an EMG pattern is abnormal requires careful consideration of normal variations and statistical limits.
    • Accurate identification of abnormal EMG patterns may necessitate population-based statistical analysis and speed normalization.
    • Distinguishing normal variability from pathology is essential for reliable clinical assessment.