Citation: Ziyang Sun, Xugang Xi, Changmin Yuan, Yong Yang, Xian Hua. Surface electromyography signal denoising via EEMD and improved wavelet thresholds[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6945-6962. doi: 10.3934/mbe.2020359
[1] | Carlo J. De Luca, Physiology and mathematics of myoelectric signals, IEEE. Trans. Biomed. Eng., 6 (1979), 313-325. |
[2] | J. Maier, A. Naber, M. Ortiz-Catalan, Improved prosthetic control based on myoelectric pattern recognition via wavelet-based de-noising, IEEE Trans. Neur. Syst. Reh. Eng., 26 (2017), 506-514. |
[3] | L. Liu, X. Chen, Z. Lu, S. Cao, D. Wu, X. Zhang, Development of an EMG-ACC-based upper limb rehabilitation training system, IEEE Trans. Neur. Syst. Reh. Eng., 25 (2016), 244-253. |
[4] | L. L. Chuang, Y. L. Chen, C. C. Chen, Y. C. Li, A. M. Wong, A. L. Hsu, et al., Effect of EMG-triggered neuromuscular electrical stimulation with bilateral arm training on hemiplegic shoulder pain and arm function after stroke: a randomized controlled trial, J. Neuroeng. Rehabil., 14 (2017), 122. doi: 10.1186/s12984-017-0332-0 |
[5] | K. Veer, Development of sensor system with measurement of surface electromyogram signal for clinical use, Optik, 127 (2016), 352-356. doi: 10.1016/j.ijleo.2015.10.072 |
[6] | C. Castellini, A. E. Fiorilla, G. Sandini, Multi-subject/daily-life activity EMG-based control of mechanical hands, J. Neuroeng. Rehabil., 6 (2009), 1-11. doi: 10.1186/1743-0003-6-1 |
[7] | R. E. Johnson, K. P. Kording, L. J. Hargrove, J. W. Sensinger, EMG versus torque control of human-machine systems: Equalizing control signal variability does not equalize error or uncertainty, IEEE Trans. Neur. Syst. Reh. Eng., 25 (2016), 660-667. |
[8] | F. Zhang, P. Li, Z. G. Hou, Z. Lu, Y. Chen, Q. Li, et al., sEMG-based continuous estimation of joint angles of human legs by using BP neural network, Neurocomputing, 78 (2012), 139-148. doi: 10.1016/j.neucom.2011.05.033 |
[9] | G. A. Lovell, P. D. Blanch, C. J. Barnes, EMG of the hip adductor muscles in six clinical examination tests, Phys. Ther. Sport., 13 (2012), 134-140. doi: 10.1016/j.ptsp.2011.08.004 |
[10] | A. Phinyomark, P. Phukpattaranont, C. Limsakul, Fractal analysis features for weak and single-channel upper-limb EMG signals, Expert. Syst. Appl., 39 (2012), 11156-11163. doi: 10.1016/j.eswa.2012.03.039 |
[11] | S. Mallat, A wavelet tour of signal processing, Academic Press, 1999. |
[12] | D. L. Donoho, De-noising by soft-thresholding, IEEE Trans. Inf. Theory, 41 (1995), 613-627. doi: 10.1109/18.382009 |
[13] | H. Liu, W. Wang, C. Xiang, L. Han, H. Nie, A de-noising method using the improved wavelet threshold function based on noise variance estimation, Mech. Syst. Signal Process., 99 (2018), 30-46. doi: 10.1016/j.ymssp.2017.05.034 |
[14] | M. Srivastava, C. L. Anderson, J. H. Freed, A new wavelet denoising method for selecting decomposition levels and noise thresholds, IEEE Access, 4 (2016), 3862-3877. doi: 10.1109/ACCESS.2016.2587581 |
[15] | M. S. Hussain, M. B. I. Reaz, F. Mohd‐Yasin, M. I. Ibrahimy, Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction, Expert Syst., 26 (2009), 35-48. doi: 10.1111/j.1468-0394.2008.00483.x |
[16] | M. Khezri, M. Jahed, Surface electromyogram signal estimation based on wavelet thresholding technique, In 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2008), 4752-4755. |
[17] | S. Raurale, J. McAllister, J. M. del Rincon, Emg wrist-hand motion recognition system for real-time embedded platform, In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, (2019), 1523-1527. |
[18] | E. Mastinu, F. Clemente, P. Sassu, O. Aszmann, R. Brånemark, B. Håkansson, et al., Grip control and motor coordination with implanted and surface electrodes while grasping with an osseointegrated prosthetic hand, J. Neuroeng. Rehabilitation, 16 (2019), 1-10. doi: 10.1186/s12984-018-0454-z |
[19] | G. Wei, F. Tian, G. Tang, C. Wang, A wavelet-based method to predict muscle forces from surface electromyography signals in weightlifting, J. Bionic. Eng., 9 (2012), 48-58. doi: 10.1016/S1672-6529(11)60096-6 |
[20] | J. Xu, Z. Wang, C. Tan, L. Si, X. Liu, A novel denoising method for an acoustic-based system through empirical mode decomposition and an improved fruit fly optimization algorithm, Appl. Sci., 7 (2017), 215. doi: 10.3390/app7030215 |
[21] | Y. Lv, R. Yuan, G. Song, Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing, Mech. Syst. Signal Process., 81 (2016). 219-234. |
[22] | X. Zhao, M. Li, G. Song, J. Xu, Hierarchical ensemble-based data fusion for structural health monitoring, Smart Mater. Struct., 19 (2010), 045009. doi: 10.1088/0964-1726/19/4/045009 |
[23] | N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Math. Phys. Eng. Sci., 454 (1998), 903-995. doi: 10.1098/rspa.1998.0193 |
[24] | Z. Wu, N. E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adap. Data. Anal., 1 (2009), 1-41. doi: 10.1142/S1793536909000047 |
[25] | M. E. Torres, M. A. Colominas, G. Schlotthauer, P. Flandrin, A complete ensemble empirical mode decomposition with adaptive noise, In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, (2011), 4144-4147. |
[26] | B. He, Y. P. Bai, MEMS hydrophone signal denoising based on wavelet packet and CEEMDAN, Math. Pract. Theory, 46 (2016), 139-147. |
[27] | Y. Xu, M. Luo, T. Li, G. Song, ECG signal de-noising and baseline wander correction based on CEEMDAN and wavelet threshold, Sensors, 17 (2017), 2754. doi: 10.3390/s17122754 |
[28] | Y. Li, Y. Li, X. Chen, J. Yu, H. Yang, L. Wang, A new underwater acoustic signal denoising technique based on CEEMDAN, mutual information, permutation entropy, and wavelet threshold denoising, Entropy, 20 (2018), 563. doi: 10.3390/e20080563 |
[29] | A. O. Andrade, S. Nasuto, P. Kyberd, C. M. Sweeney-Reed, F. R. Van Kanijn, EMG signal filtering based on empirical mode decomposition, Biomed. Signal Process Control, 1 (2006), 44-55. |
[30] | X. Zhang, P. Zhou, Filtering of surface EMG using ensemble empirical mode decomposition, Med. Eng. Phys., 35 (2013), 537-542. doi: 10.1016/j.medengphy.2012.10.009 |
[31] | W. Jiao, Z. Li, D. Wang, A method for wavelet threshold denoising of seismic data based on CEEMD, Geophys. Prospect. Pet., 53 (2014), 164-172. |
[32] | J. X. Zhang, Q. H. Zhong, Y. P. Dai, The determination of the threshold and the decomposition order in threshold de-noising method based on wavelet transform, In Proceedings of the CSEE, 24 (2004), 118-122. |
[33] | W. B. Wang, X. D. Zhang, X. L. Wang, Empirical mode decomposition de-noising method based on principal component analysis, Acta. Elec. Sin., 41 (2013), 1425-1430. |
[34] | M. Ortiz-Catalan, R. Brånemark, B. Håkansson, BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms, Source Code Biol. Med., 8 (2013), 11. |