A computational model of surface electromyography signal alterations after spinal cord injury

Objective. Spinal cord injury (SCI) can cause significant impairment and disability with an impact on the quality of life for individuals with SCI and their caregivers. Surface electromyography (sEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing neuromuscular changes resulting from SCI. The mechanisms of the sEMG signal characteristic changes due to SCI are multi-faceted and difficult to study in vivo. In this study, we utilized well-established computational models to characterize changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the SCI. Approach. Starting from existing models for motor neuron pool organization and motor unit action potential generation for healthy neuromuscular systems, we implemented scenarios to model damages to upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit. 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 Kendall Rank Correlation analysis. Main results. The commonly used amplitude-based sEMG features (such as mean absolute values and root mean square) cannot differentiate between injury scenarios, but a broader set of features (including autoregression and cepstrum coefficients) provides greater specificity to the type of damage present. Significance. We introduce a novel approach to mechanistically relate sEMG features (often underused in SCI research) to different types of neuromuscular alterations that may occur after SCI. This work contributes to the further understanding and utilization of sEMG in clinical applications, which will ultimately improve patient outcomes after SCI.

Automated summary

This study used computational models to investigate changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the injury. The results showed that commonly used amplitude-based sEMG features cannot differentiate between injury scenarios, but a broader set of features provides greater specificity to the type of damage present.