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多尺度肌间耦合网络分析 被引量:1

Multiple-scale intermuscular coupling network analysis
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摘要 为了更加准确有效地从复杂网络的角度理解不同时空层次的肌间耦合情况,本文提出了一种新的多尺度肌间耦合网络分析方法。将多元变分模态分解(MVMD)与Copula互信息(Copula MI)相结合,构建了基于MVMD-Copula MI的肌间耦合网络模型,通过节点强度、聚类系数等网络参数分析了健康人数据集伸手运动过程中上肢多块肌肉在不同时频尺度上的肌间耦合特性。实验结果表明,在分解出的6个时频尺度上,肌间耦合特性存在明显区别。具体为:肱三头肌与三角肌中束、三角肌后束的耦合强度相对较高,肌间功能联系紧密;而肱二头肌在该运动下独立于其他肌肉。结果体现了肌间耦合网络具有尺度差异性,而MVMD-Copula MI能够定量刻画多尺度肌间耦合强度关系,具有良好的应用前景。 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.
作者 吴亚婷 佘青山 高云园 谭同才 范影乐 WU Yating;SHE Qingshan;GAO Yunyuan;TAN Tongcai;FAN Yingle(School of Automation,Hangzhou Dianzi University,Hangzhou 310018,P.R.China;Department of Rehabilitation Medicine,Zhejiang Provincial People’s Hospital,People’s Hospital of Hangzhou Medical College,Hangzhou 310014,P.R.China)
出处 《生物医学工程学杂志》 EI CAS CSCD 北大核心 2021年第4期742-752,共11页 Journal of Biomedical Engineering
基金 国家自然科学基金资助项目(61871427,61971168) 山东省重点研发计划(重大科技创新工程)项目(2019JZZY021005)。
关键词 多元变分模态分解 互信息 COPULA 肌间耦合网络 multivariate variational modal decomposition mutual information Copula intermuscular coupling network
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  • 1孟宗,戴桂平,刘彬.基于EMD时频分析方法的性能研究[J].传感技术学报,2006,19(4):1029-1032. 被引量:20
  • 2PENG HC, LONG F, DING C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and minredundancy [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226 - 1238.
  • 3HACINE-GHARBI A, RAVIER P, HARBS R, et al. Low bias histogram-based estimation of mutual information for feature selection [J]. Pattern Recognition Letters, 2012, 33(16): 1302- 1308.
  • 4MOON Y, RAJAGOPALAN B, LALL U. Estimation of mutual information using kernel density estimators [J]. Physical Review E, 1995, 52(3): 2318 - 2321.
  • 5KRASKOV A, STOGBAUER H, GRASSBERGER P. Estimating mutual information [J]. Physical Review E, 2004, 69(6): 066138.
  • 6DARBELLAY G A, VAJDA I. Estimation of the information by an adaptive partitioning of the observation space [J]. IEEE Transactions on Information Theory, 1999,45(4): 1315 -1321.
  • 7DAUB C 0, STEUER R, SELBIG J, et al. Estimating mutual information using B-spline functions - an improved similarity measure for analysing gene expression data [J]. BMC Bioinformatics, 2004, 5(1): 118.
  • 8SUZUKI T, SUGIYAMA M, SESE J, et al. Approximating mutual information by maximum likelihood density ratio estimation [C]I/New Challenges for Feature Selection in Data Mining and Knowledge Discovery, JMLR Workshop and Conference Proceedings. Brookline, USA: Microtome Publishing, 2008, 4: 5 - 20.
  • 9ENDRES D, FOLDIAK P. Bayesian bin distribution inference and mutual information [J]. IEEE Transactions on Information Theory, 2005,51(11): 3766 - 3779.
  • 10SHANNON C E. A mathematical theory of communication [J]. Acm Sigmobile Mobile Computing and Communications Review, 2001, 5(1): 3 - 55.

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