Combinational spectral band activation complexity: Uncovering hidden neuromuscular firing dynamics in EMG

Nicholas J. Napoli, Anthony R. Mixco, Savannah V. Wooten, Marco Jacopetti, Joseph F. Signorile

Research output: Contribution to journalArticlepeer-review

Abstract

A new approach, Combinational Spectral Band Activation Complexity (CSB-AC), that extracts the neuromuscular firing dynamics of surface electromyography (sEMG) signals by applying entropic methods in a multi-dimensional fashion by analyzing the signals temporally, spectrally, and intensity dynamics simultaneously is presented. The CSB-AC signal processing approach introduces a methodology that highlights that a small amount of key fiducial points embedded within the sEMG, 1000x reduction in EMG data, are only needed to show statistically significant changes of the neuromuscular firing dynamics. CSB-AC was compared to the more generalized sample entropy method to demonstrate physiological differences between cohorts and baseline mapping between the two measurements. Results indicated significant differences between CSB-AC and sample entropy regardless of age groups for tibialis anterior and plantar flexion muscles (gastrocnemius medialis, gastrocnemius lateralis, and soleus). Significant differences were found between older and younger subject groups for the gastrocnemius medialis and soleus with the older adults having higher complexity values. CSB-AC produces greater complexity than sample entropy, where this sparser set of data holds paramount information for describing neuromuscular firing and should not be ignored. CSB-AC, accomplishes this by simultaneously assessing the complexity of sEMG's time, intensity, and spectral content, where latent properties of neuromuscular dynamics within this unique set of sparse sEMG data points are critical to characterizing neuromuscular firing.

Original languageEnglish (US)
Article number102891
JournalBiomedical Signal Processing and Control
Volume70
DOIs
StatePublished - Sep 2021
Externally publishedYes

Keywords

  • Electromyography (EMG)
  • Entropy
  • Wavelets

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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