Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding

Noisy Signals Detection in High Density EMG


  • Khalil Ullah Department of Software Engineering, University of Malakand, Chakdara, Pakistan
  • Khalid Shah Department of Computer Science, Bannu University of Science and Technology, Bannu, Pakistan



EMG, Noisy Channel, Power Line Interference, Statistical Thresholding


Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.


D. Farina, and R. Merletti. A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE T Bio-Med Eng.48(6):637– 646 (2001).

A. J. Fuglevand, D. A. Winter, A. E. Patla. Models of recruitment and rate coding organization in motorunit pools. J europhysiol.70(6):2470–2488 (1993).

Y. Zhang, B. Liu, X. Ji, D. Huang. Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition. Neural Process Lett. 1–14 (2016).

R. Imoto, M. Migita, M. Toda, S. Sakurazawa, J. Akita, K. Kondo, et al. Preliminaly study on coordinated movement mechanism of multiple muscle using wavelet coherence analysis. IEEE 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 605–608 (2016).

F. Y. Wu, F. Tong, Z. Yang. EMG signal enhancement based on ICA decomposition and wavelet transform. Appl Soft Comput.43:561–571 (2016).

A. R. Al-Qawasmi, K. Daqrouq. ECG signal enhancement using wavelet transform. WSEAS Transactions on Biology and iomedicine.7:62–71 (2010).

C. K. Chui. An introduction to wavelets. Elsevier; 2016.

S. Fitzgibbon, D. DeLos Angeles, T. Lewis, D. Powers, T. Grummett , E. Whitham, et al. Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis. Clin Neurophysiol. 127(3):1781–1793 (2016).

D. Dharmaprani, H. K. Nguyen, T. W. Lewis, D. DeLos Angeles, J. O. Willoughby, K. J. Pope. A comparison of independent component

analysis algorithms and measures to discriminate between EEG and artifact components. IEEE 38th International Conference of the Medicine and Biology Society (EMBC), 825–828 (2016).

T. He, G. Clifford, L. Tarassenko. Application of independent component analysis in removing artefacts from the electrocardiogram. Neural Comput Appl. 15(2):105–116 (2006).

G. Tsolis, T. D. Xenos. Signal denoising using empirical mode decomposition and higher order statistics. International Journal of Signal Processing, Image Processing and Pattern Recognition.4(2):91– 106 (2011).

V. K. Mishra, V. Bajaj, A. Kumar, G. K. Singh. Analysis of ALS and normal EMG signals based on empirical mode decomposition. Iet Sci Meas Technol.10(8):963–971 (2016).

A. Siddiqi, S.P. Arjunan, D. K. Kumar. Age related neuromuscular changes in sEMG of m. Tibialis Anterior using higher order statistics (Gaussianity & linearity test). IEEE 38th Annual International Conference of the Medicine and Biology Society (EMBC), 3638–3641 (2016).

A. Keller, U. Blumenthal, G. Kar. Classification, and computation of dependencies for distributed management. Fifth IEEE Symposium on Computers and Communications; 78–83 (2000).

D. F. Specht. Probabilistic neural networks. Neural networks. 3(1):109–118 (1990).

M. Li, Y. Liang, L. Yang, H. Wang, Z. Yang, K. Zhao, Z. Shang, H. Wan, Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals, Computers in Biology and Medicine (2020), doi: https:// compbiomed.2019.103572 17. R. H. Chowdhury, M. B. Reaz, A. MABM, A. A. Bakar, K. Chellappan, T. G. Chang. Surface electromyography signal processing and classification techniques. Sensors. 13(9):12431–12466 (2013).

A. S. Khaing, Z. M. Naing. Quantitative Investigation of Digital Filters in Electrocardiogram with Simulated Noises. IJIEE.1(3):210 (2011).

H. Nolan, R. Whelan, R. Reilly. FASTER: fully automated statistical thresholding for EEG artifact rejection. J Neurosci Meth. 192(1):152–162 (2010).

E. A. Clancy, N. Hogan. Probability density of the surface electromyogram and its relation to amplitude detectors. IEEE T Bio-Med Eng.46(6):730–739


A. Ba ̆rbulescu, C. Serban, C. Maftei. Evaluation of Hurst exponent for precipitation time series. Latest Trends on Computers. 2:590– 595 (2010).

E. Lukhanina, I. Karaban, N. Berezetskaya. Diagnosis of Parkinson’s Disease by Electrophysiological Methods. INTECH (2011).

R. Adler, R. Feldman , M. Taqqu . A practical guide to heavy tails: statistical techniques and applications. Springer Science & Business Media, USA (1998).

C. K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, A. L. Goldberger. Mosaic organization of DNA nucleotides. Phys Rev E.49(2):1685 (1994).

D. Farina, L. Mesin, S. Martina, R. Merletti. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE T Bio-Med Eng.51(3):415– 426 (2004).



2021-08-31 — Updated on 2021-08-31


How to Cite

Ullah, K., & Shah, K. (2021). Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding : Noisy Signals Detection in High Density EMG . Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 58(1), 61–70.