Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue
Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue
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DOI：
10.1109/jbhi.2023.3234960
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Rory O'Keeffe;Seyed Yahya Shirazi;Jinghui Yang;Sarmad Mehrdad;Smita Rao;S. F. Atashzar
中科院分区：
文献类型：
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作者：
Rory O'Keeffe;Seyed Yahya Shirazi;Jinghui Yang;Sarmad Mehrdad;Smita Rao;S. F. Atashzar
已经探索了使用表面肌电检测肌肉疲劳的可能性，并提出了多个生物标志物，如中值频率。然而，文献中有相互矛盾的报道，导致对疲劳的生物标志物的理解不一致。因此，对用于疲劳检测的统计稳健的基于表面肌电信号的生物标记物的需求尚未得到满足。这篇论文首次展示了非参数肌肉网络可靠地检测疲劳相关变化的优越能力。7名健康志愿者完成了一项下肢锻炼方案，其中包括在完成疲惫的腿部按压练习之前和之后30秒的坐立练习。使用Spearman的功率相关性构建了一个非参数肌肉网络，并显示出与疲劳相关的网络指标(程度、加权聚集系数(WCC))非常可靠的下降。网络指标在群体层面(学位：P<；0.001)、个体学科层面(学位：P<；0.035)和特定肌肉层面(学位：P<；0.017)上均有显著下降。关于特定肌肉的平均连接度的下降，所有七名受试者都遵循了群体趋势。与所提出的非参数肌肉网络所获得的稳健结果相比，经典的光谱时间测量在特定的肌肉和个体受试者水平上显示出不同的趋势。因此，本论文首次表明，非参数肌肉网络是一种可靠的疲劳生物标志物，具有广泛的应用前景。
The possibility of muscle fatigue detection using surface electromyography has been explored and multiple biomarkers, such as median frequency, have been suggested. However, there are contradictory reports in the literature which results in an inconsistent understanding of the biomarkers of fatigue. Thus, there is an unmet need for a statistically robust sEMG-based biomarker for fatigue detection. This paper, for the first time, demonstrates the superior capability of a non-parametric muscle network to reliably detect fatigue-related changes. Seven healthy volunteers completed a lower limb exercise protocol, which consisted of 30s of a sit-to-stand exercise before and after the completion of fatiguing leg press sets. A non-parametric muscle network was constructed, using Spearman’s power correlation and showed a very reliable decrease in network metrics associated with fatigue (degree, weighted clustering coefficient (WCC)). The network metrics displayed a significant decrease at the group level (degree, WCC: p < 0.001), individual subject level (degree: p < 0.035 WCC: p < 0.004) and particular muscle level (degree: p < 0.017). Regarding the decrease in mean degree connectivity at particular muscles, all seven subjects followed the group trend. In contrast to the robust results achieved by the proposed non-parametric muscle network, classical spectrotemporal measurements showed heterogeneous trends at the particular muscle and individual subject levels. Thus, this paper for the first time shows that non-parametric muscle network is a reliable biomarker of fatigue and could be used in a broad range of applications.