Poster (Technology Innovation) ID 1984794

Cervical spinal cord injury (SCI) can cause significant impairment and disability with an impact on individuals’ quality of life and independence. Surface electromyography (SEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing the impact from SCI. The mechanisms of SCI damage on SEMG signal characteristics are multi-faceted and difficult to study in vivo. Use validated computational models to characterize changes in SEMG signal after SCI and identify SEMG features that are sensitive and specific to the impact from different aspects of SCI. Starting from existing computational models for motor neuron pool organization and for motor unit action potential generation for healthy neuromuscular systems, we set up scenarios to model alterations in upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit after SCI. After simulating SEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on SEMG features using the Pearson correlation between a feature and the extent of a given disruption. Commonly used amplitude-based SEMG features cannot differentiate between injury scenarios. A broader set of features provides greater specificity to the type of damage present. We demonstrated a novel approach to mechanistically relate SEMG features to different types of neuromuscular alterations after SCI. This work contributes to a deeper understanding and exploitation of SEMG in clinical applications, which will ultimately improve patient outcomes after SCI.