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Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
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下肢非参数功能肌肉网络:测试复试可靠性分析

Rory O'Keeffe, Jinghui Yang, Sarmad Mehrdad

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |July 3, 2023
    PubMed
    概括

    功能性肌肉网络分析显示,在下肢任务中,测试复试可靠性很高. 这些网络指标为康复提供了可靠的生物标志物,优于传统的表面电肌图 (sEMG) 测量.

    科学领域:

    • 生物医学工程 生物医学工程
    • 神经科学是一个神经科学.
    • 康复科学 康复科学 康复科学

    背景情况:

    • 功能性肌肉网络分析显示,检测肌肉间同步性变化是有前途的.
    • 这些网络措施的可靠性,特别是在会议之间和会议内,仍然在很大程度上未确立.
    • 之前的研究主要集中在健康受试者和神经疾病上.

    研究的目的:

    • 为了评估非参数的下肢功能肌肉网络的测试-重新测试可靠性.
    • 为了比较网络指标的可靠性与传统的表面电肌图 (sEMG) 测量.
    • 在健康个体中评估控制 (坐立) 和轻度控制 (走路) 任务的可靠性.

    主要方法:

    • 15名健康的受试者参加了两次测试,分别是在不同的日子.
    • 使用14个表面电肌图 (sEMG) 传感器记录肌肉活动.
    • 计算了网络指标 (程度,集群系数) 和sEMG指标 (RMS,MDF) 的类内相关系数 (ICC).

    主要成果:

    • 与传统的sEMG测量 (RMS,MDF) 相比,功能性肌肉网络在会话之间显示出更高的可靠性.
    • 在网络指标和经典指标之间观察到可靠性的统计学上显著差异.
    • 来自功能性肌肉网络的地形指标在少量会话中获得了可靠的测量结果.

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    结论:

    • 来自功能肌肉网络的地形指标可以可靠地用于多会话观测.
    • 这些指标可靠量化下肢任务中的协同性肌肉间同步性.
    • 功能性肌肉网络指标显示出作为康复监测可靠生物标志物的潜力.