基于时变copula互信息的肌间耦合分析

doi:10.3969/j.issn.0258-8021.2022.02.002

Abstract
Figure/Table
References
Related Citation (4)

Download: PDF (12508 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To measure the relationship between different muscles accurately and reasonably under the regulation of central nervous system is a challenging research topic. Based on the time-varying copula function and combining with the entropy theory, a time-varying copula mutual information (MI) estimation method was proposed in this paper and applied it to the coupling analysis of sEMG signals of biceps brachii (BB) and triceps brachii (TB) in the characteristic frequency bands (theta, alpha and beta) during wrist flexion (WF) and wrist extension (WE) movement of 10 subjects. Meanwhile, the method was compared with the static copula function to verify its effectiveness. The data we used were derived from Ninapro DB4. Experimental results show that compared to the static copula function, the time-varying copula function has a better fitting degree for the intermuscular dependent structure. There was a significant frequency band difference in the intermuscular coupling strength described by the time-varying copula MI (P<0.05), which was specifically expressed as: the higher the frequency band, the lower the intermuscular coupling strength (WF: 0.075 7~0.214 7 bit. WE: 0.078 0~0.237 3 bit), while the static copula MI incorrectly underestimates the intermuscular coupling strength. In conclusion, the time-varying copula MI provided an advanced theoretical guidance method for intermuscular coupling analysis and showed a very broad application prospect.

Key words： time-varying copula function dependent structure mutual information characteristic frequency band intermuscular coupling

Received: 18 January 2021

PACS:

R318

Corresponding Authors: ^*E-mail: qsshe@hdu.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Wang Hongan
	She Qingshan
	Ma Yuliang
	Kong Wanzeng
	Tian Yuping

Cite this article:

Wang Hongan,She Qingshan,Ma Yuliang, et al. Time-Varying Copula Mutual Information for Intermuscular Coupling Analysis[J]. Chinese Journal of Biomedical Engineering, 2022, 41(2): 140-150.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.02.002 OR http://cjbme.csbme.org/EN/Y2022/V41/I2/140

[1] 陈玲玲,李珊珊,刘作军,等. 基于表面肌电的下肢肌肉功能网络构建及其应用研究 [J]. 自动化学报, 2017, 43(3): 407-417.
[2] Xu Yuhang, Mcclelland VM, Cvetkovi Z, et al. Cortico-muscular coherence with time lag with application to delay estimation [J]. IEEE Transactions on Biomedical Engineering, 2017, 64(3): 588-600.
[3] 杜义浩,杨文娟,姚文轩,等. 中风患者康复运动中多通道肌间耦合特性分析 [J]. 生物医学工程学杂志, 2019, 36(5): 720-727.
[4] 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 [J]. 生物医学工程学杂志, 2014, 31(5): 971-977.
[5] Grosse P, Cassidy MJ, Brown P. EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications [J]. Clinical Neurophysiology, 2002, 113(10): 1523-1531.
[6] Chen Xiaoling, Xie Ping, Zhang Yuanyuan, et al. Abnormal functional corticomuscular coupling after stroke [J]. Neuroimage: Clinical, 2018, 19: 147-159.
[7] 谢平,宋妍,郭子晖,等. 中风康复运动中肌肉异常耦合分析 [J]. 生物医学工程学杂志, 2016, 33(2): 244-254.
[8] Faes L, Marinazzo D, Jurysta F, et al. Linear and non-linear brain–heart and brain–brain interactions during sleep [J]. Physiological Measurement, 2015, 36(4): 683-698.
[9] Ma Jian, Sun Zengqi. Mutual information is copula entropy [J]. Tsinghua Science & Technology, 2011, 16(1): 51-54.
[10] Fabrizio D, Juan FS, Carlo S. A topological proof of Sklar's theorem [J]. Applied Mathematics Letters, 2013, 26(9): 945-948.
[11] Fisher N. Copulas. Encyclopedia of Statistical Sciences [M]. New York: John Wiley Sons, 1997, 1: 159-163.
[12] Ji Xinlong, Zhou Lu. Risk Correlation based on time-varying copula function and extreme value theory [J]. Theoretical Economics Letters, 2017, 7(7): 2213-2229.
[13] Patton AJ. Modeling time-varying exchange rate dependence using conditional copula [R]. UCSD Discussion Paper No. 01-09, 2001.
[14] Feng Ying, Shi Peng, Qu Simin, et al. Nonstationary flood coincidence risk analysis using time-varying copula functions [J]. Scientific Reports, 2020, 10(1): 3395-3407.
[15] Jiang Cong, Xiong Lihua, Xu Chongyu, et al. Bivariate frequency analysis of nonstationary low-flow series based on the time-varying copula [J]. Hydrological Processes, 2014, 29(6): 1521-1534.
[16] Loaiza-Maya R, Smith MS. Real-time macroeconomic forecasting with a heteroscedastic inversion copula [J]. Journal of Business & Economic Stats, 2020, 38(2): 470-486.
[17] Pizzolato S, Tagliapietra L, Cognolato M, et al. Comparison of six electromyography acquisition setups on hand movement classification tasks [J]. PLoS ONE, 2017, 12(10): e0186132.
[18] Krause V, Wach C, Südmeyer M, et al. Cortico-muscular coupling and motor performance are modulated by 20 Hz transcranial alternating current stimulation (tACS) in Parkinson's disease [J]. Frontiers in Human Neuroscience. 2014, 7: 928-938.
[19] Sun Fuqiang, Zhang Wendi, Wang Ning, et al. A copula entropy approach to dependence measurement for multiple degradation processes [J]. Entropy, 2019, 21(8): 724-743.
[20] 郑行,佘青山,高云园,等. 基于传递熵与广义偏定向相干性的肌间耦合分析 [J]. 航天医学与医学工程, 2018, 31(5): 538-544.
[21] Silva RD, Lopes HF. Copula, marginal distributions and model selection: a Bayesian note [J]. Statistics Computing, 2008, 18: 313-320.
[22] Joo K, Shin JY, Heo JH. Modified maximum pseudo likelihood method of copula parameter estimation for skewed hydrometeorological data [J]. Water, 2020, 12(4): 1182-1204.
[23] Boonstra TW, Danna-Dos-Santos A, Xie HB, et al. Muscle networks: Connectivity analysis of EMG activity during postural control [J]. Scientific Reports, 2015, 5(3): 17830-17843.
[24] Gao Zhixian, Chen Lin, Xiong Qiliang, et al. Degraded synergistic Recruitment of sEMG oscillations for cerebral palsy infants crawling [J]. Frontiers in Neurology, 2018, 9:760-771.
[25] Jaiser SR, Baker MR, Naker SN. Intermuscular coherence in normal adults: Variability and changes with age [J]. PLoS ONE, 2016, 11(2): e0149029.
[26] Chang Kuangliang. The time-varying and asymmetric dependence between crude oil spot and futures markets: evidence from the mixture copula-based ARJI-GARCH model [J]. Economic Modelling, 2012, 29(6): 2298-2309.