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Feasibility of High-Density Surface Electromyography for the Detection of Neuromuscular Disorders in Children
Eduardo Martinez-Valdes1, Ignacio Contreras-Hernandez1, Ragul Selvamoorthy1
1School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, UK.
Introduction/Aims:
Diagnosing neuromuscular disorders in children is challenging. Concentric needle electromyography (CNEMG) is the standard for electrophysiological assessments but has limitations in pediatric populations. High-density surface electromyography (HDsEMG) provides a noninvasive technique with superior spatial resolution, enabling the identification and analysis of motor unit (MU) firing dynamics throughout the entire period of MU activity. This study assessed the feasibility of HDsEMG MU decomposition in children and explored parameters that differentiate neuropathy, myopathy, and normal findings.
Methods:
One hundred children (mean age 9.1 years, standard deviation [SD] 5.1) underwent CNEMG followed by HDsEMG. EMG signals were decomposed into individual MU spike trains, and MU yield, as well as firing properties (mean discharge rate [MDR], discharge rate variability [DRV]) were analyzed across diagnostic groups. Furthermore, correlations were assessed between MU action potential parameters obtained from CNEMG (MU amplitude and duration) and those obtained from HDsEMG.
Results:
MUs were reliably identified in 86.0% of children, with an average of 7 (4.2) MUs per participant. Among MU firing parameters, DRV was significantly higher in children with myopathy (p = 0.005). Additionally, MU duration from HDsEMG correlated weakly with CNEMG values (r = 0.31) and successfully discriminated myopathy from normal and neuropathic groups (p = 0.02).
Discussion:
HDsEMG MU decomposition is feasible in children with neuromuscular disorders, providing valuable insights into MU firing and MU action potential properties. This technique has the potential to improve diagnosis and monitoring of pediatric neuromuscular conditions. Nevertheless, further signal processing refinements are warranted to enhance its discriminative capacity for detecting neuromuscular disorders in children.
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