Multiple-scale intermuscular coupling network analysis_Journal of Biomedical Engineering

Authors：

WU Yating ¹ ,  SHE Qingshan ¹ , GAO Yunyuan ¹ ,  TAN Tongcai ² , FAN Yingle ¹

1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, P.R.China;
2. Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou 310014, P.R.China;

Corresponding?author：

SHE Qingshan, Email: qsshe@hdu.edu.cn; TAN Tongcai, Email: 29ttc@sina.com

Keywords：

multivariate variational modal decomposition; mutual information; Copula; intermuscular coupling network

DOI：

10.7507/1001-5515.202009023

Video：

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In order to more accurately and effectively understand the intermuscular coupling of different temporal and spatial levels from the perspective of complex networks, a new multi-scale intermuscular coupling network analysis method was proposed in this paper. The multivariate variational modal decomposition (MVMD) and Copula mutual information (Copula MI) were combined to construct an intermuscular coupling network model based on MVMD-Copula MI, and the characteristics of intermuscular coupling of multiple muscles of upper limbs in different time-frequency scales during reaching exercise in healthy subjects were analyzed by using the network parameters such as node strength and clustering coefficient. The experimental results showed that there are obvious differences in the characteristics of intermuscular coupling in the six time-frequency scales. Specifically, the triceps brachii (TB) had relatively high coupling strength with the middle deltoid (MD) and posterior deltoid (PD), and the intermuscular function was closely connected. However, the biceps brachii (BB) was independent of other muscles. The intermuscular coupling network had scale differences. MVMD-Copula MI can quantitatively describe the relationship of multi-scale intermuscular coupling strength, which has good application prospects.

Citation： WU Yating, SHE Qingshan, GAO Yunyuan, TAN Tongcai, FAN Yingle. Multiple-scale intermuscular coupling network analysis. Journal of Biomedical Engineering, 2021, 38(4): 742-752. doi: 10.7507/1001-5515.202009023 Copy

1.	Chen Shuang, Han Bingxue, Yan Zhi, et al. Functional acupuncture intervention mechanism of upper limb dysfunction after stroke. Tradit Chin Med, 2019, 8(1): 49-52.
2.	Du Yihao, Yang Wenjuan, Yao Wenxuan, et al. Analysis of multichannel intermuscular coupling characteristics during rehabilitation after stroke. J Biomed Eng, 2019, 36(5): 720-727.
3.	Chen Xiaoling, Xie Ping, Zhang Yuanyuan, et al. Abnormal functional corticomuscular coupling after stroke. Neuroimage Clin, 2018, 19: 147-159.
4.	Qassim Y T, Cutmore T R, James D A, et al. Wavelet coherence of EEG signals for a visual oddball task. Comput Biol Med, 2013, 43(1): 23-31.
5.	馬鵬剛, 佘青山, 高云園, 等. 基于多元經驗模態分解-傳遞熵的腦肌電信號耦合分析. 傳感技術學報, 2018, 31(6): 88-98.
6.	G?tz T, Stadler L, Fraunhofer G, et al. A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation. J Neural Eng, 2017, 14(1): 016011(1)-016011(17).
7.	Wu Z H, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal, 2009, 1(1): 1-41.
8.	Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process, 2014, 62(3): 531-544.
9.	謝平, 楊芳梅, 李欣欣, 等. 基于變分模態分解-傳遞熵的腦肌電信號耦合分析. 物理學報, 2016, 65(11): 285-293.
10.	Rehman N U, Mandic D P. Filter bank property of multivariate empirical mode decomposition. IEEE Trans Signal Process, 2011, 59(5): 2421-2426.
11.	Rehman N U, Aftab H. Multivariate variational mode decomposition. IEEE Trans Signal Process, 2019, 67(23): 6039-6052.
12.	陳玲玲, 李珊珊, 劉作軍, 等. 基于表面肌電的下肢肌肉功能網絡構建及其應用研究. 自動化學報, 2017, 43(3): 407-417.
13.	Duggento A, Passamonti L, Valenza G, et al. Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI. Sci Rep, 2018, 8(6): 424-438.
14.	杜義浩, 齊文靖, 鄒策, 等. 基于變分模態分解-相干分析的肌間耦合特性. 物理學報, 2017, 66(6): 345-354.
15.	李素姣, 劉蘇, 藍賀, 等. 腦肌電信號同步耦合分析方法研究進展. 生物醫學工程學雜志, 2019, 36(2): 334-337, 342.
16.	Betzel R F, Avena-Koenigsberger A, Go?i J, et al. Generative models of the human connectome. NeuroImage, 2016, 124(1): 1054-1064.
17.	Boonstra T W, Danna-Dos-Santos A, Xie H, et al. Muscle networks: Connectivity analysis of EMG activity during postural control. Sci Rep, 2016, 5(1): 17830-17830.
18.	Kerkman J N, Andreas D, Gollo L L, et al. Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales. Sci Adv, 2018, 4(6): 497-508.
19.	陳玲玲, 張存, 宋曉偉, 等. 面向外骨骼機器人的肌肉功能網絡構建及分析. 生物醫學工程學雜志, 2019, 36(4): 565-571.
20.	Ma Jian, Sun Zengqi. Mutual information is copula entropy. Tsinghua Sci Technol, 2011, 16(1): 51-54.
21.	Ince R A A, Giordano B L, Kayser C, et al. A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Hum Brain Mapp, 2016, 38(3): 1541-1573.
22.	Xiao Feiyun, Yang Decai, Lv Zhongming, et al. Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals. Future Gener Comput Syst, 2020, 110(1): 1023-1036.
23.	Bashan A, Bartsch R P, Kantelhardt J W, et al. Network physiology reveals relations between network topology and physiological function. Nat Commun, 2012, 3(1): 702-722.
24.	Han Min, Liu Xiaoxin. Mutual information estimation based on Copula entropy. Control Theory Appl, 2013, 30(7): 875-879.
25.	Liang Jing, Zhao Haixing, Zhang Ke, et al. The characteristic polynomial of generalized laplace matrix of several kinds of graphs. Adv Appl Math, 2017, 6(6): 763-767.
26.	Thuraisingham R A. Estimating EEG network parameters using mutual information. Brain Connect, 2018, 8(5): 311-317.
27.	Caliandro P, Vecchio F, Miraglia F, et al. Small-world characteristics of cortical connectivity changes in acute stroke. Neurorehabil Neural Repair, 2017, 31(1): 81-94.
28.	Sede?o-noda A, Colebrook M. A biobjective Dijkstra algorithm. Eur J Oper Res, 2019, 276(1): 106-118.
29.	Israely S, Leisman G, Machluf C, et al. Direction modulation of muscle synergies in ahand-reaching task. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(1): 24-27.
30.	Han Xiao, She Qingshan, Gao Yunyuan, et al. Feature extraction of EEG based on NA-MEMD and mutual information. Chin J Sens Actuators, 2016, 29(8): 1140-1148.
31.	黃威, 高云園, 張迎春, 等. 基于非負矩陣分解和復雜網絡的肌間耦合分析. 航天醫學與醫學工程, 2019, 32(2): 159-166.
32.	謝平, 宋妍, 郭子暉, 等. 中風康復運動中肌肉異常耦合分析. 生物醫學工程學雜志, 2016, 33(2): 244-254.

1. Chen Shuang, Han Bingxue, Yan Zhi, et al. Functional acupuncture intervention mechanism of upper limb dysfunction after stroke. Tradit Chin Med, 2019, 8(1): 49-52.
2. Du Yihao, Yang Wenjuan, Yao Wenxuan, et al. Analysis of multichannel intermuscular coupling characteristics during rehabilitation after stroke. J Biomed Eng, 2019, 36(5): 720-727.
3. Chen Xiaoling, Xie Ping, Zhang Yuanyuan, et al. Abnormal functional corticomuscular coupling after stroke. Neuroimage Clin, 2018, 19: 147-159.
4. Qassim Y T, Cutmore T R, James D A, et al. Wavelet coherence of EEG signals for a visual oddball task. Comput Biol Med, 2013, 43(1): 23-31.
5. 馬鵬剛, 佘青山, 高云園, 等. 基于多元經驗模態分解-傳遞熵的腦肌電信號耦合分析. 傳感技術學報, 2018, 31(6): 88-98.
6. G?tz T, Stadler L, Fraunhofer G, et al. A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation. J Neural Eng, 2017, 14(1): 016011(1)-016011(17).
7. Wu Z H, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal, 2009, 1(1): 1-41.
8. Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process, 2014, 62(3): 531-544.
9. 謝平, 楊芳梅, 李欣欣, 等. 基于變分模態分解-傳遞熵的腦肌電信號耦合分析. 物理學報, 2016, 65(11): 285-293.
10. Rehman N U, Mandic D P. Filter bank property of multivariate empirical mode decomposition. IEEE Trans Signal Process, 2011, 59(5): 2421-2426.
11. Rehman N U, Aftab H. Multivariate variational mode decomposition. IEEE Trans Signal Process, 2019, 67(23): 6039-6052.
12. 陳玲玲, 李珊珊, 劉作軍, 等. 基于表面肌電的下肢肌肉功能網絡構建及其應用研究. 自動化學報, 2017, 43(3): 407-417.
13. Duggento A, Passamonti L, Valenza G, et al. Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI. Sci Rep, 2018, 8(6): 424-438.
14. 杜義浩, 齊文靖, 鄒策, 等. 基于變分模態分解-相干分析的肌間耦合特性. 物理學報, 2017, 66(6): 345-354.
15. 李素姣, 劉蘇, 藍賀, 等. 腦肌電信號同步耦合分析方法研究進展. 生物醫學工程學雜志, 2019, 36(2): 334-337, 342.
16. Betzel R F, Avena-Koenigsberger A, Go?i J, et al. Generative models of the human connectome. NeuroImage, 2016, 124(1): 1054-1064.
17. Boonstra T W, Danna-Dos-Santos A, Xie H, et al. Muscle networks: Connectivity analysis of EMG activity during postural control. Sci Rep, 2016, 5(1): 17830-17830.
18. Kerkman J N, Andreas D, Gollo L L, et al. Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales. Sci Adv, 2018, 4(6): 497-508.
19. 陳玲玲, 張存, 宋曉偉, 等. 面向外骨骼機器人的肌肉功能網絡構建及分析. 生物醫學工程學雜志, 2019, 36(4): 565-571.
20. Ma Jian, Sun Zengqi. Mutual information is copula entropy. Tsinghua Sci Technol, 2011, 16(1): 51-54.
21. Ince R A A, Giordano B L, Kayser C, et al. A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Hum Brain Mapp, 2016, 38(3): 1541-1573.
22. Xiao Feiyun, Yang Decai, Lv Zhongming, et al. Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals. Future Gener Comput Syst, 2020, 110(1): 1023-1036.
23. Bashan A, Bartsch R P, Kantelhardt J W, et al. Network physiology reveals relations between network topology and physiological function. Nat Commun, 2012, 3(1): 702-722.
24. Han Min, Liu Xiaoxin. Mutual information estimation based on Copula entropy. Control Theory Appl, 2013, 30(7): 875-879.
25. Liang Jing, Zhao Haixing, Zhang Ke, et al. The characteristic polynomial of generalized laplace matrix of several kinds of graphs. Adv Appl Math, 2017, 6(6): 763-767.
26. Thuraisingham R A. Estimating EEG network parameters using mutual information. Brain Connect, 2018, 8(5): 311-317.
27. Caliandro P, Vecchio F, Miraglia F, et al. Small-world characteristics of cortical connectivity changes in acute stroke. Neurorehabil Neural Repair, 2017, 31(1): 81-94.
28. Sede?o-noda A, Colebrook M. A biobjective Dijkstra algorithm. Eur J Oper Res, 2019, 276(1): 106-118.
29. Israely S, Leisman G, Machluf C, et al. Direction modulation of muscle synergies in ahand-reaching task. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(1): 24-27.
30. Han Xiao, She Qingshan, Gao Yunyuan, et al. Feature extraction of EEG based on NA-MEMD and mutual information. Chin J Sens Actuators, 2016, 29(8): 1140-1148.
31. 黃威, 高云園, 張迎春, 等. 基于非負矩陣分解和復雜網絡的肌間耦合分析. 航天醫學與醫學工程, 2019, 32(2): 159-166.
32. 謝平, 宋妍, 郭子暉, 等. 中風康復運動中肌肉異常耦合分析. 生物醫學工程學雜志, 2016, 33(2): 244-254.

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Multiple-scale intermuscular coupling network analysis

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