ArticleMuscle & nerve2026
Feasibility of High-Density Surface Electromyography for the Detection of Neuromuscular Disorders in Children.
Article in Muscle & nerve, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
INTRODUCTION/
aimsDiagnosing 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.
methodsOne 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.
resultsMUs 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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