[Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):938-944. doi: 10.7507/1001-5515.202303065.
[Article in Chinese]

Abstract

An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8-13 Hz), beta frequency band (14-30 Hz) and gamma frequency band (31-60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.

深入了解步行过程中的下肢肌肉协作机制是提高神经肌肉功能障碍患者步态康复疗效的关键。本文研究了步行速度的变化对下肢肌肉协同模式及肌肉功能网络的影响。招募了8名健康受试者分别以三种不同速度在跑步机上执行步行任务,同步采集右下肢8块肌肉的表面肌电信号(sEMG),通过非负矩阵分解(NNMF)方法提取肌肉协同模式,利用互信息(MI)方法分别构建alpha频段(8~13 Hz)、beta频段(14~30 Hz)和gamma频段(31~60 Hz)肌肉功能网络,引入复杂网络分析方法量化不同网络差异。肌肉协同分析提取到5个肌肉协同模式,步行速度的变化没有改变肌肉协同的数量,但导致了肌肉权值的变化;肌肉网络分析发现在同一速度下,高频段具有更低的全局效率和聚类系数,随着步行速度的增加,局部肌肉之间的连接强度增加。研究结果表明不同速度的步行运动存在不同的肌肉协同模式和肌肉功能网络,本研究为探索不同步行速度下肌肉协同机制提供了新的视野,有望为神经肌肉功能障碍患者的步态功能评估提供理论支撑。.

Keywords: Muscle functional network; Muscle synergy; Surface electromyography; Walking movement.

Publication types

  • English Abstract

MeSH terms

  • Electromyography
  • Gait / physiology
  • Humans
  • Muscle, Skeletal* / physiology
  • Walking / physiology
  • Walking Speed*

Grants and funding

国家重点研发计划基金资助项目(2017YFB1401200);河北省自然科学基金资助项目(F2021201002);河北省高等学校科学技术研究项目(ZD2020146)