From Single Fiber Action Potential to Surface Electromyographic Signal: A Simulation Study

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• 作者：Noureddine Messaoudi (20) (21)
  Ra茂s El鈥檋adi Bekka (21)
  
  20. Physics Department ; Sciences Faculty ; University of Boumerdes ; 35000 ; Boumerdes ; Algeria
  21. Lab. LIS ; Electronics Department ; Faculty of Technology ; University of S茅tif 1 ; 19000 ; S茅tif ; Algeria
  
• 关键词：Detection ; Limb muscle ; SEMG Simulation ; spatial filter
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9043
• 期：1
• 页码：315-324
• 全文大小：305 KB
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  8. Mesin, L., Farina, D. (2006) An analytical model for surface EMG generation in volume conductors with smooth conductivity variations. IEEE Trans. Biomed. Eng. 53: pp. 773-779 CrossRef
  9. Mesin, L. (2006) Simulation of surface EMG signals for a multi-layer volume conductor with triangular model of the muscle tissue. IEEE Trans Biomed Eng 53: pp. 2177-2184 CrossRef
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  12. Lowery, M.M., Stoykov, N.S., Taflove, A., Kuiken, T.A. (2002) A multiple-layer finite-element model of the surface EMG signal. IEEE Trans. Biomed. Eng. 49: pp. 446-454 CrossRef
  13. Mesin, L., Joubert, M., Hanekom, T., Merletti, R., Farina, D. (2005) A Finite Element Model for Describing the Effect of Muscle Shortening on Surface EMG. IEEE Trans. Biomed. Eng. 53: pp. 593-719 CrossRef
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• 作者单位：Bioinformatics and Biomedical Engineering
• 丛书名：978-3-319-16482-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

The purpose of this investigation was to simulate surface electromyographic (EMG) signals generated in a cylindrical multilayer volume conductor constituted by bone (isotropic) muscle (anisotropic), fat (isotropic) and skin (isotropic) layers. This simulation was based on the distributions of the: MFs within each motor unit (MU), motor units (MUs) within the muscle, diameters of all activated MUs, conduction velocities of all activated MUs, lengths of all MFs, firing rates (FRs) of all recruited MUs, inter-spike intervals (ISIs) and the starting recruitment times of the activated MUs. A MU is composed of an alpha motor neuron and connected MFs, thus the action potential generated in each MU (MUAP) is the sum of the action potentials generated from MFs (SFAPs) belonging to that particular MU. The simulation of surface EMG signal began first by simulating SFAP and then the simulation of the MUAP. The non uniform repetition of the MUAP with a firing rate gives the MUAP train (MUAPT). Finally, the surface EMG signal is the sum of non-synchronized MUAP trains of active MUs. Four filters were used to detect the surface EMG signals. Simulations results show that the amplitude and shape of surface EMG signals depend on the filter used for recording.