Poster (Technology Innovation) ID 1984794

IF 2.4 Q1 REHABILITATION Topics in Spinal Cord Injury Rehabilitation Pub Date : 2023-09-01 DOI:10.46292/sci23-1984794s
Guijin Li, G. Balbinot, Julio C Furlan, Sukhvinder Kalsi-Ryan, J. Zariffa
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Abstract

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.
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海报(技术创新) ID 1984794
颈椎脊髓损伤(SCI)会导致严重的损伤和残疾,影响个人的生活质量和独立性。表面肌电图(SEMG)是一种测量肌肉活动的灵敏而无创的技术,在捕捉 SCI 造成的影响方面已显示出巨大的潜力。SCI 对 SEMG 信号特征的损伤机制是多方面的,很难在体内进行研究。 使用经过验证的计算模型来描述 SCI 后 SEMG 信号的变化,并确定对 SCI 不同方面的影响具有敏感性和特异性的 SEMG 特征。 从现有的运动神经元池组织计算模型和健康神经肌肉系统的运动单元动作电位产生计算模型出发,我们设置了一些情景,以模拟上运动神经元、下运动神经元和每个运动单元内肌纤维数量在 SCI 后的变化。模拟每个场景的 SEMG 信号后,我们提取了时域和频域特征,并使用特征与特定干扰程度之间的皮尔逊相关性研究了 SCI 干扰对 SEMG 特征的影响。 常用的基于振幅的 SEMG 特征无法区分不同的损伤情况。更广泛的特征集可为损伤类型提供更高的特异性。 我们展示了一种新方法,从机理上将 SEMG 特征与 SCI 后不同类型的神经肌肉改变联系起来。这项工作有助于在临床应用中更深入地了解和利用 SEMG,最终改善 SCI 患者的预后。
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来源期刊
CiteScore
3.20
自引率
3.40%
发文量
33
期刊介绍: Now in our 22nd year as the leading interdisciplinary journal of SCI rehabilitation techniques and care. TSCIR is peer-reviewed, practical, and features one key topic per issue. Published topics include: mobility, sexuality, genitourinary, functional assessment, skin care, psychosocial, high tetraplegia, physical activity, pediatric, FES, sci/tbi, electronic medicine, orthotics, secondary conditions, research, aging, legal issues, women & sci, pain, environmental effects, life care planning
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