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本文引用的文献

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Phase-Dependent Modulation of Muscle Activity and Intermuscular Coupling During Walking in Patients After Stroke.中风后患者行走过程中肌肉活动和肌间耦合的相位依赖性调制
IEEE Trans Neural Syst Rehabil Eng. 2023;31:1119-1127. doi: 10.1109/TNSRE.2023.3238758. Epub 2023 Feb 6.
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Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling.时频最大信息系数方法及其在皮质运动功能耦合中的应用。
IEEE Trans Neural Syst Rehabil Eng. 2020 Nov;28(11):2515-2524. doi: 10.1109/TNSRE.2020.3028199. Epub 2020 Nov 6.
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[Construction and analysis of muscle functional network for exoskeleton robot].外骨骼机器人肌肉功能网络的构建与分析
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2019 Aug 25;36(4):565-572. doi: 10.7507/1001-5515.201803059.
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Group-level cortical and muscular connectivity during perturbations to walking and standing balance.群组水平皮质和肌肉连接在行走和站立平衡的扰动中。
Neuroimage. 2019 Sep;198:93-103. doi: 10.1016/j.neuroimage.2019.05.038. Epub 2019 May 18.
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Can activities of daily living contribute to EMG normalization for gait analysis?日常生活活动能否有助于肌电图正常化以进行步态分析?
PLoS One. 2017 Apr 3;12(4):e0174670. doi: 10.1371/journal.pone.0174670. eCollection 2017.
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Effects of chronic neck-shoulder pain on normalized mutual information analysis of surface electromyography during functional tasks.慢性颈肩痛对功能任务期间表面肌电图标准化互信息分析的影响。
Clin Neurophysiol. 2016 Sep;127(9):3110-3117. doi: 10.1016/j.clinph.2016.06.015. Epub 2016 Jun 25.
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Muscle networks: Connectivity analysis of EMG activity during postural control.肌肉网络:姿势控制过程中肌电图活动的连通性分析。
Sci Rep. 2015 Dec 4;5:17830. doi: 10.1038/srep17830.
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Clin Biomech (Bristol). 2016 Jan;31:65-73. doi: 10.1016/j.clinbiomech.2015.09.019. Epub 2015 Oct 8.
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Shared muscle synergies in human walking and cycling.人类行走和骑行中的共享肌肉协同作用。
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基于表面肌电信号分析不同步行速度下的肌肉协同作用和肌肉功能网络

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

作者信息

Cui Caihong, Miao Huacong, Liang Tie, Liu Xiuling, Liu Xiaoguang

机构信息

Department of Rehabilitation Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei 071002, P. R. China.

Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, Hebei 071002, P. R. China.

出版信息

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

DOI:10.7507/1001-5515.202303065
PMID:37879923
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10600430/
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.

摘要

深入了解步行过程中下肢肌肉协调机制是提高神经肌肉功能障碍患者步态康复效果的关键。本文研究步行速度变化对下肢肌肉协同模式和肌肉功能网络的影响。招募八名健康受试者在跑步机上以三种不同速度执行步行任务,并同步采集右下肢八块肌肉的表面肌电信号(sEMG)。采用非负矩阵分解(NNMF)方法提取肌肉协同模式,采用互信息(MI)方法构建α频段(8 - 13Hz)、β频段(14 - 30Hz)和γ频段(31 - 60Hz)肌肉功能网络,并引入复杂网络分析方法量化不同网络之间的差异。肌肉协同分析提取出5种肌肉协同模式,步行速度的变化并未改变肌肉协同的数量,但导致了肌肉权重的变化。肌肉网络分析发现,在相同速度下,高频段的全局效率和聚类系数较低。随着步行速度的增加,局部肌肉之间的连接强度也增加。结果表明,不同步行速度下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调机制提供了新视角,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持。