Lower-Limb Non-Parametric Functional Muscle Network: Test-Retest Reliability Analysis
Lower-Limb Non-Parametric Functional Muscle Network: Test-Retest Reliability Analysis
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DOI：
10.1109/tnsre.2023.3291748
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发表时间：
2023-07
影响因子：
4.9
通讯作者：
Graduate Student Member Ieee Rory O’Keeffe;Jinghui Yang;Graduate Student Member Ieee Sarmad Mehrdad;Smita Rao;S. M. I. S. Farokh Atashzar-S.-M.-I.-S.-Farokh-Atashzar-2225980810
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文献类型：
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作者：
Graduate Student Member Ieee Rory O’Keeffe;Jinghui Yang;Graduate Student Member Ieee Sarmad Mehrdad;Smita Rao;S. M. I. S. Farokh Atashzar-S.-M.-I.-S.-Farokh-Atashzar-2225980810
近年来，功能肌肉网络分析引起了人们的极大兴趣，它有望对肌肉间同步性的变化具有高度的敏感性，主要研究对象是健康人，最近研究的对象是患有神经系统疾病(如中风引起的疾病)的患者。尽管有很有希望的结果，但功能肌肉网络措施的会话间和会话内的可靠性仍有待建立。在这里，我们第一次质疑和评估了非参数下肢功能肌肉网络在受控和轻度受控任务中的重测可靠性，即在健康受试者中分别进行坐到站和地面行走。15名受试者(8名女性)在两个不同的日子里参加了两次会议。用14个表面肌电信号(SEMG)传感器记录肌肉活动。对于不同的网络指标，会话内和会话间试验的组内相关系数(ICC)被量化，包括程度和加权聚类系数。为了与常用的经典表面肌电信号进行比较，还计算了表面肌电信号均方根(RMS)和中值频率(MDF)的可靠性。ICC分析显示，肌肉网络的节间可靠性更高，与经典测量方法相比有统计学上的显著差异。本文提出，从功能肌肉网络产生的地形图指标可以可靠地用于多时段的观察，确保了高可靠性，以量化控制和轻度控制的下肢任务的协同肌肉间同步性的分布。此外，地形网络指标达到可靠测量所需的会话次数很少，这表明在康复期间有可能成为生物标记物。
Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.