Analysis of multichannel intermuscular coupling characteristics during rehabilitation after stroke_Journal of Biomedical Engineering

Authors：

DU Yihao ¹ , YANG Wenjuan ¹ , YAO Wenxuan ¹ , QI Wenjing ¹ , CHEN Xiaoling ¹ , XIE Boduo ² ,  XIE Ping ¹

1. Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;
2. Department of Rehabilitation Medicine, the No. 281 Hospital of Chinese People’s Liberation Army, Qinhuangdao, Hebei 066100, P.R.China;

Corresponding author：

XIE Ping, Email: pingx@ysu.edu.cn

Keywords：

stroke patient; electromyography; intermuscular coupling; coherence; non-negative matrix factorization

DOI：

10.7507/1001-5515.201807025

Video：

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To better analyze the problem of abnormal neuromuscular coupling related to motor dysfunction for stroke patients, the functional coupling of the multichannel electromyography (EMG) were studied and the difference between stroke patients and healthy subjects were further analyzed to explore the pathological mechanism of motor dysfunction after stroke. Firstly, the cross-frequency coherence (CFC) analysis and non-negative matrix factorization (NMF) were combined to construct a CFC-NMF model to study the linear coupling relationship in bands and the nonlinear coupling characteristics in different frequency ratios during elbow flexion and extension movement. Furthermore, the significant coherent area and sum of cross-frequency coherence were respectively calculated to quantitatively describe the intermuscular linear and nonlinear coupling characteristics. The results showed that the linear coupling relationship between multichannel muscles was different in frequency bands and the overall coupling was stronger in low frequency band. The linear coupling strength of the stroke patients was lower than that of the healthy subjects in different frequency bands especially in beta and gamma bands. For the nonlinear coupling, the intermuscular coupling strength of stroke patients in different frequency ratios was significantly lower than that of the healthy subjects, and the coupling strength in the frequency ratio 1∶2 was higher than that in the frequency ratio 1∶3. This method can provide a theoretical basis for exploring the intermuscular coupling mechanism of patients with motor dysfunction.

Citation： DU Yihao, YANG Wenjuan, YAO Wenxuan, QI Wenjing, CHEN Xiaoling, XIE Boduo, XIE Ping. Analysis of multichannel intermuscular coupling characteristics during rehabilitation after stroke. Journal of Biomedical Engineering, 2019, 36(5): 720-727. doi: 10.7507/1001-5515.201807025 Copy

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3.	Tao Wen, Zhang Xu, Chen Xiang, et al. Multi-scale complexity analysis of muscle coactivation during gait in children with cerebral palsy. Front Hum Neurosci, 2015, 9: 367.
4.	Grosse P, Cassidy M J, Brown P. EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications. Clinical Neurophysiology, 2002, 113(10): 1523-1531.
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7.	Wang Jing, Fang Yuxing, Wang Xiao, et al. Enhanced gamma activity and cross-frequency interaction of resting-state electroencephalographic oscillations in patients with Alzheimer’s disease. Front Aging Neurosci, 2017, 9: 243.
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9.	Brown P, Farmer S F, Halliday D M, et al. Coherent cortical and muscle discharge in cortical myoclonus. Brain, 1999, 122(Pt 3): 461-472.
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11.	Danna-Dos Santos A, Poston B, Jesunathadas M, et al. Influence of fatigue on hand muscle coordination and EMG-EMG coherence during three-digit grasping. J Neurophysiol, 2010, 104(6): 3576-3587.
12.	杜义浩, 齐文靖, 邹策, 等. 基于变分模态分解-相干分析的肌间耦合特性. 物理学报, 2017, 66(6): 341-350.
13.	Grosse P, Edwards M, Tijssen M A J, et al. Patterns of EMG-EMG coherence in limb dystonia. Mov Disord, 2004, 19(7): 758-769.
14.	Kisiel-Sajewicz K, Fang Yin, Hrovat K, et al. Weakening of synergist muscle coupling during reaching movement in stroke patients. Neurorehabil Neural Repair, 2011, 25(4): 359-368.
15.	Fisher K M, Zaaimi B, Williams T L, et al. Beta-band intermuscular coherence: a novel biomarker of upper motor neuron dysfunction in motor neuron disease. Brain, 2012, 135(9): 2849-2864.
16.	Yang Yuan, Schouten A C, Solis-Escalante T, et al. Probing the nonlinearity in neural systems using cross-frequency coherence framework. IFAC-PapersOnline, 2015, 48(28): 1386-1390.
17.	Lee D D, Seung H S. Algorithms for non-negative matrix factorization// Advances in Neural Information Processing Systems. Vancouver: Oregon State University, 2001: 556-562.
18.	Nikias C L, Mendel J M. Signal processing with higher-order spectra. IEEE Signal Process Mag, 1993, 10(3): 10-37.
19.	Rosenberg J R, Amjad AM, Breeze P, et al. The Fourier approach to the identification of functional coupling between neuronal spike trains. Prog Biophys Mol Biol, 1989, 53(1): 1-31.
20.	谢平, 宋妍, 郭子晖, 等. 中风康复运动中肌肉异常耦合分析. 生物医学工程学杂志, 2016, 33(2): 244-254.
21.	马培培, 陈迎亚, 杜义浩, 等. 中风康复运动中脑电-肌电相干性分析. 生物医学工程学杂志, 2014, 34(5): 971-977.
22.	Gwin J T, Ferris D P. Beta- and gamma-range human lower limb corticomuscular coherence. Front Hum Neurosci, 2012, 6: 258.
23.	Mima T, Toma K, Koshy B, et al. Coherence between cortical and muscular activities after subcortical stroke. Stroke, 2001, 32(11): 2597-2601.
24.	Hyafil A, Giraud A L, Fontolan L, et al. Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci, 2015, 38(11): 725-740.
25.	Yang Yuan, Solis-Escalante T, van de Ruit M, et al. Nonlinear coupling between cortical oscillations and muscle activity during isotonic wrist flexion. Front Comput Neurosci, 2016, 10: 126.

1. Summers J J, Kagerer F A, Garry M I, et al. Bilateral and unilateral movement training on upper limb function in chronic stroke patients: a TMS study. J Neurol Sci, 2007, 252(1): 76-82.
2. Baker S N. Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol, 2007, 17(6): 649-655.
3. Tao Wen, Zhang Xu, Chen Xiang, et al. Multi-scale complexity analysis of muscle coactivation during gait in children with cerebral palsy. Front Hum Neurosci, 2015, 9: 367.
4. Grosse P, Cassidy M J, Brown P. EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications. Clinical Neurophysiology, 2002, 113(10): 1523-1531.
5. Roberts J A, Robinson P A. Quantitative theory of driven nonlinear brain dynamics. Neuroimage, 2012, 62(3): 1947-1955.
6. 谢平, 杨芳梅, 李欣欣, 等. 基于变分模态分解-传递熵的脑肌电信号耦合分析. 物理学报, 2016, 65(11): 277-285.
7. Wang Jing, Fang Yuxing, Wang Xiao, et al. Enhanced gamma activity and cross-frequency interaction of resting-state electroencephalographic oscillations in patients with Alzheimer’s disease. Front Aging Neurosci, 2017, 9: 243.
8. Yang Yuan, Solis-Escalante T, van der Helm F C T, et al. A generalized coherence framework for detecting and characterizing nonlinear interactions in the nervous system. IEEE Trans Biomed Eng, 2016, 63(12): 2629-2637.
9. Brown P, Farmer S F, Halliday D M, et al. Coherent cortical and muscle discharge in cortical myoclonus. Brain, 1999, 122(Pt 3): 461-472.
10. Grosse P, Guerrini R, Parmeggiani L, et al. Abnormal corticomuscular and intermuscular coupling in high-frequency rhythmic myoclonus. Brain, 2003, 126(2): 326-342.
11. Danna-Dos Santos A, Poston B, Jesunathadas M, et al. Influence of fatigue on hand muscle coordination and EMG-EMG coherence during three-digit grasping. J Neurophysiol, 2010, 104(6): 3576-3587.
12. 杜义浩, 齐文靖, 邹策, 等. 基于变分模态分解-相干分析的肌间耦合特性. 物理学报, 2017, 66(6): 341-350.
13. Grosse P, Edwards M, Tijssen M A J, et al. Patterns of EMG-EMG coherence in limb dystonia. Mov Disord, 2004, 19(7): 758-769.
14. Kisiel-Sajewicz K, Fang Yin, Hrovat K, et al. Weakening of synergist muscle coupling during reaching movement in stroke patients. Neurorehabil Neural Repair, 2011, 25(4): 359-368.
15. Fisher K M, Zaaimi B, Williams T L, et al. Beta-band intermuscular coherence: a novel biomarker of upper motor neuron dysfunction in motor neuron disease. Brain, 2012, 135(9): 2849-2864.
16. Yang Yuan, Schouten A C, Solis-Escalante T, et al. Probing the nonlinearity in neural systems using cross-frequency coherence framework. IFAC-PapersOnline, 2015, 48(28): 1386-1390.
17. Lee D D, Seung H S. Algorithms for non-negative matrix factorization// Advances in Neural Information Processing Systems. Vancouver: Oregon State University, 2001: 556-562.
18. Nikias C L, Mendel J M. Signal processing with higher-order spectra. IEEE Signal Process Mag, 1993, 10(3): 10-37.
19. Rosenberg J R, Amjad AM, Breeze P, et al. The Fourier approach to the identification of functional coupling between neuronal spike trains. Prog Biophys Mol Biol, 1989, 53(1): 1-31.
20. 谢平, 宋妍, 郭子晖, 等. 中风康复运动中肌肉异常耦合分析. 生物医学工程学杂志, 2016, 33(2): 244-254.
21. 马培培, 陈迎亚, 杜义浩, 等. 中风康复运动中脑电-肌电相干性分析. 生物医学工程学杂志, 2014, 34(5): 971-977.
22. Gwin J T, Ferris D P. Beta- and gamma-range human lower limb corticomuscular coherence. Front Hum Neurosci, 2012, 6: 258.
23. Mima T, Toma K, Koshy B, et al. Coherence between cortical and muscular activities after subcortical stroke. Stroke, 2001, 32(11): 2597-2601.
24. Hyafil A, Giraud A L, Fontolan L, et al. Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci, 2015, 38(11): 725-740.
25. Yang Yuan, Solis-Escalante T, van de Ruit M, et al. Nonlinear coupling between cortical oscillations and muscle activity during isotonic wrist flexion. Front Comput Neurosci, 2016, 10: 126.

Journal of Biomedical Engineering

Analysis of multichannel intermuscular coupling characteristics during rehabilitation after stroke

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