A new method for high-density surface electromyography decomposition in dynamic muscle contraction_Journal of Biomedical Engineering

Authors：

 HE Jinbao ¹ , GUAN Binglei ¹ , HUANG Kai ² , LUO Zaifei ¹

1. The School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, Zhejiang 315211, P.R.China;
2. Ningbo First Hospital, Ningbo, Zhejiang 315010, P.R.China;

Corresponding author：

HE Jinbao, Email: perfecthjb@126.com

Keywords：

motor unit; surface electromyography; dynamic signal decomposition; spatial location

DOI：

10.7507/1001-5515.202104020

Video：

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In this paper, a new surface electromyography (sEMG) signal decomposition method based on spatial location is proposed for the high-density sEMG signals in dynamic muscle contraction. Firstly, according to the waveform correlation of each muscle motor units (MU) in each channel, the firing times are extracted, and then the firing times are classified by the spatial location of MU. The MU firing trains are finally obtained. The simulation results show that the accuracy rate of a single MU firing train after classification is more than 91.67%. For real sEMG signals, the accuracy rate to find a same MU by the “two source” method is over (88.3 ± 2.1)%. This paper provides a new idea for dynamic sEMG signal decomposition.

Citation： HE Jinbao, GUAN Binglei, HUANG Kai, LUO Zaifei. A new method for high-density surface electromyography decomposition in dynamic muscle contraction. Journal of Biomedical Engineering, 2021, 38(6): 1081-1086. doi: 10.7507/1001-5515.202104020 Copy

1.	Liu Y, Ning Y, Li S, et al. Three-dimensional innervation zone imaging from multi-channel surface EMG recordings. Int J Neural Syst, 2015, 25(6): 1550024.
2.	Chen M, Zhang X, Lu Z, et al. Two-source validation of progressive FastICA peel-off for automatic surface EMG decomposition in human first dorsal interosseous muscle. Int J Neural Syst, 2018, 28(9): 1850019.
3.	Holobar A, Zazula D. Multichannel blind source separation using convolution kernel compensation. IEEE T Signal Proces, 2007, 55(9): 4487-4496.
4.	Ning Y, Zhu X, Zhu S, et al. Surface EMG decomposition based on K-means clustering and convolution kernel compensation. IEEE J Biomed Health Inform, 2015, 19(2): 471-477.
5.	He Jinbao, Luo Zaifei. A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings. Journal of Clinical Neuroscience, 2018, 54: 146-151.
6.	Glaser V, Holobar A. On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju Island:Korea, 2017: 430-433.
7.	Glaser V, Holobar A. Impact of motor unit action potential components on the motor unit identification from dynamic high-density surface electromyograms//2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). Shanghai:China, 2017: 90-93.
8.	Deluca C J, Chang S S, Roy S H, et al. Decomposition of surface EMG signals from cyclic dynamic contractions. J Neurophysiol, 2015, 113(6): 1941-1951.
9.	宁勇, 李津蓉, 朱善安. 基于FastICA和通道间相关的表面肌电信号分解研究. 航天医学与医学工程, 2017, 30(3): 191-197.
10.	Ning Y, Zhao Y, Juraboev A, et al. Multichannel surface EMG decomposition based on measurement correlation and LMMSE. J Healthc Eng, 2018: 2347589.
11.	He Jinbao, Yi Xinhua, Luo Zaifei. A simulation study on the depth information of motor units. Biomed Eng Lett, 2016, 6(2): 80-86.
12.	Andreassen S, Rosenfalck A. Relationship of intracellular and extracellular action potentials of skeletal muscle fibers. Crit Rev Bioeng, 1981, 6(4): 267-306.
13.	Merletti R, Lo Conte L, Avignone E, et al. Modeling of surface myoelectric signals-Part I: Model implementation. IEEE Trans Biomed Eng, 1999, 46(7): 810-820.
14.	Ning Y, Dias N, Li X, et al. Improve computational efficiency and estimation accuracy of multi-channel surface EMG decomposition via dimensionality reduction. Comput Biol Med, 2019, 112: 103372.
15.	何金保, 骆再飞, 管冰蕾, 等. 基于运动单元的肌肉力估计新方法. 航天医学与医学工程, 2015, 28(5): 313-318.
16.	Ou Wanmei, Hämäläinen M S, Golland P. A distributed spatio-temporal EEG/MEG inverse solver. Neuroimage, 2009, 44(3): 932-946.

1. Liu Y, Ning Y, Li S, et al. Three-dimensional innervation zone imaging from multi-channel surface EMG recordings. Int J Neural Syst, 2015, 25(6): 1550024.
2. Chen M, Zhang X, Lu Z, et al. Two-source validation of progressive FastICA peel-off for automatic surface EMG decomposition in human first dorsal interosseous muscle. Int J Neural Syst, 2018, 28(9): 1850019.
3. Holobar A, Zazula D. Multichannel blind source separation using convolution kernel compensation. IEEE T Signal Proces, 2007, 55(9): 4487-4496.
4. Ning Y, Zhu X, Zhu S, et al. Surface EMG decomposition based on K-means clustering and convolution kernel compensation. IEEE J Biomed Health Inform, 2015, 19(2): 471-477.
5. He Jinbao, Luo Zaifei. A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings. Journal of Clinical Neuroscience, 2018, 54: 146-151.
6. Glaser V, Holobar A. On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju Island:Korea, 2017: 430-433.
7. Glaser V, Holobar A. Impact of motor unit action potential components on the motor unit identification from dynamic high-density surface electromyograms//2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). Shanghai:China, 2017: 90-93.
8. Deluca C J, Chang S S, Roy S H, et al. Decomposition of surface EMG signals from cyclic dynamic contractions. J Neurophysiol, 2015, 113(6): 1941-1951.
9. 宁勇, 李津蓉, 朱善安. 基于FastICA和通道间相关的表面肌电信号分解研究. 航天医学与医学工程, 2017, 30(3): 191-197.
10. Ning Y, Zhao Y, Juraboev A, et al. Multichannel surface EMG decomposition based on measurement correlation and LMMSE. J Healthc Eng, 2018: 2347589.
11. He Jinbao, Yi Xinhua, Luo Zaifei. A simulation study on the depth information of motor units. Biomed Eng Lett, 2016, 6(2): 80-86.
12. Andreassen S, Rosenfalck A. Relationship of intracellular and extracellular action potentials of skeletal muscle fibers. Crit Rev Bioeng, 1981, 6(4): 267-306.
13. Merletti R, Lo Conte L, Avignone E, et al. Modeling of surface myoelectric signals-Part I: Model implementation. IEEE Trans Biomed Eng, 1999, 46(7): 810-820.
14. Ning Y, Dias N, Li X, et al. Improve computational efficiency and estimation accuracy of multi-channel surface EMG decomposition via dimensionality reduction. Comput Biol Med, 2019, 112: 103372.
15. 何金保, 骆再飞, 管冰蕾, 等. 基于运动单元的肌肉力估计新方法. 航天医学与医学工程, 2015, 28(5): 313-318.
16. Ou Wanmei, Hämäläinen M S, Golland P. A distributed spatio-temporal EEG/MEG inverse solver. Neuroimage, 2009, 44(3): 932-946.

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