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Noise Confiscation from sEMG Through Enhanced Adaptive Filtering Based on Evolutionary Computing

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Abstract

Electromyogram (EMG) signal is the electrical form of muscular activity that could be used to diagnose myopathy and neuropathy disorders. Several artefacts are getting imposed on the EMG during the recording process, affecting its performance. This paper proposes a novel noise clampdown method for surface electromyogram signals to employ powerful evolutionary algorithms such as cat swarm optimisation (CSO), binary gravitational search algorithm (BGSA) and spotted hyena optimisation (SHO) for the optimisation purpose of the adaptive filter. The proposed technique has been appraised on records from the standard database, corrupted by baseline wander, electrode motion, power-line noise and different additive white Gaussian noise (AWGN) levels. The potency of the proposed method is studied in terms of standard metrics, namely signal-to-noise ratio (SNR), normalised root mean square error, mean squared error (MSE), peak reconstruction error, mean difference and maximum error. Results exemplify that the proposed scheme outpaces the benchmark algorithm-based techniques with an average SNR of 87.618 dB and MSE of 3.91E−10, across different datasets, in contrast to the recently employed noise reduction algorithms at 10 dB AWGN.

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References

  1. M.K. Ahirwal, A. Kumar, G.K. Singh, EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimisation algorithms. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(6), 1491–1504 (2013). https://doi.org/10.1109/tcbb.2013.119

    Article  Google Scholar 

  2. M.K. Ahirwal, A. Kumar, G.K. Singh, Adaptive filtering of EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digit. Signal Process. 25(1), 164–172 (2014). https://doi.org/10.1016/j.dsp.2013.10.019

    Article  Google Scholar 

  3. R.H. Chowdhury, M.B.I. Reaz, M.A.B.M. Ali, A.A.A. Bakar, K. Chellappan, T.G. Chang, Surface electromyography signal processing and classification techniques. Sensors 13, 12431–12466 (2013). https://doi.org/10.3390/s130912431

    Article  Google Scholar 

  4. L. Demanet, L. Ying, Wave atoms and sparsity of oscillatory patterns. Appl. Comput. Harmon. Anal. 23(3), 368–387 (2007). https://doi.org/10.1016/j.acha.2007.03.003

    Article  MathSciNet  MATH  Google Scholar 

  5. B. Derrick, P. White, Comparing two samples from an individual likert question. Int. J. Math. Stat. 18(3), 1–13 (2017)

    MathSciNet  Google Scholar 

  6. G. Dhiman, V. Kumar, Spotted hyena optimiser: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70 (2017). https://doi.org/10.1016/j.advengsoft.2017.05.014

    Article  Google Scholar 

  7. C. Elisei-Iliescu, C. Stanciu, C. Paleologu, J. Benesty, C. Anghel, S. Ciochină, Efficient recursive least-squares algorithms for the identification of bilinear forms. Digit. Signal Process. 83, 280–296 (2018). https://doi.org/10.1016/j.dsp.2018.09.005

    Article  Google Scholar 

  8. D. Farina, F. Negro, Accessing the neural drive to muscle and translation to neurorehabilitation technologies. IEEE Rev. Biomed. Eng. 5, 3–14 (2012). https://doi.org/10.1109/rbme.2012.2183586

    Article  Google Scholar 

  9. A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, PCh. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). https://doi.org/10.1161/01.cir.101.23.e215

    Article  Google Scholar 

  10. T. Grujic, A. Kuzmanic, Denoising of surface EMG signals: A comparison of wavelet and classical digital filtering procedures. Technol. Healthc. 12(2), 130–135 (2004)

    Google Scholar 

  11. A. Jafarifarmand, M.-A. Badamchizadeh, S. Khanmohammadi, M.A. Nazari, B.M. Tazehkand, Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach. Biomed. Signal Process. Control 31, 199–210 (2017). https://doi.org/10.1016/j.bspc.2016.08.006

    Article  Google Scholar 

  12. S. Jain, M.K. Ahirwal, A. Kumar, V. Bajaj, G.K. Singh, QRS detection using adaptive filters: a comparative study. ISA Trans. 66, 362–375 (2017). https://doi.org/10.1016/j.isatra.2016.09.023

    Article  Google Scholar 

  13. L. Janjanam, S.K. Saha, R. Kar, D. Mandal, An efficient identification approach for highly complex nonlinear systems using the evolutionary computing-based Kalman filter. Int. J. Electron. Commun. 138, 153–890 (2021). https://doi.org/10.1016/j.aeue.2021.153890

    Article  Google Scholar 

  14. R.K. Joseph, G. Titus, M.S. Sudhakar, Effective EMG denoising using a hybrid model based on WAT and GARCH. Biomed. Signal Process. Control 45, 305–312 (2018). https://doi.org/10.1016/j.bspc.2018.05.040

    Article  Google Scholar 

  15. S. Mirjalili, A. Lewis, Adaptive gbest-guided gravitational search algorithm. Neural. Comput. Appl. 25, 1569–1584 (2014). https://doi.org/10.1007/s00521-014-1640-y

    Article  Google Scholar 

  16. P.K. Mohapatra, P.K. Jena, S.K. Bisoi, S.K. Rout, S.P. Panigrahi, Channel equalisation as an optimisation problem, in International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (2016), pp. 1158–1163. https://doi.org/10.1109/scopes.2016.7955623

  17. G.B. Moody, W.E. Muldrow, R.G. Mark, A noise stress test for arrhythmia detectors. Comput. Cardiol. 11, 381–384 (1984). https://doi.org/10.13026/c2hs3t

    Article  Google Scholar 

  18. B. Nagasirisha, V.V.K.D.V. Prasad, Noise removal from EMG signal using adaptive enhanced squirrel search algorithm. Fluct. Noise Lett. 19(4), 2050039 (2020). https://doi.org/10.1142/s021947752050039x

    Article  Google Scholar 

  19. G.R. Naik, S.E. Selvan, H.T. Nguyen, Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 734–743 (2016). https://doi.org/10.1109/tnsre.2015.2454503

    Article  Google Scholar 

  20. H.S. Pal, A. Kumar, A. Vishwakarma, M.K. Ahirwal, Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimisation techniques. Biomed. Signal Process. Control 78, 103932 (2022). https://doi.org/10.1016/j.bspc.2022.103932

    Article  Google Scholar 

  21. J. Piskorowski, Time-efficient removal of power-line noise from EMG signals using IIR notch filters with non-zero initial conditions. Biocybern. Biomed. Eng. 33(3), 171–178 (2013). https://doi.org/10.1016/j.bbe.2013.07.006

    Article  Google Scholar 

  22. M. Rakshit, S. Das, An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomed. Signal Process. Control 40, 140–148 (2018). https://doi.org/10.1016/j.bspc.2017.09.020

    Article  Google Scholar 

  23. S.K. Saha, S.P. Ghoshal, R. Kar, D. Mandal, Design and simulation of FIR bandpass and bandstop filters using gravitational search algorithm. Memet. Comput. 5(4), 311–321 (2013). https://doi.org/10.1007/s12293-013-0122-6

    Article  Google Scholar 

  24. S.K. Saha, S.P. Ghoshal, R. Kar, D. Mandal, Cat swarm optimisation algorithm for optimal linear phase FIR filter design. ISA Trans. 52(6), 781–794 (2013). https://doi.org/10.1016/j.isatra.2013.07.009

    Article  Google Scholar 

  25. I.-M. Skavhaug, K.R. Lyons, A. Nemchuk, S.D. Muroff, S.S. Joshi, Learning to modulate the partial powers of a single sEMG power spectrum through a novel human-computer interface. Hum. Mov. Sci. 47, 60–69 (2016). https://doi.org/10.1016/j.humov.2015.12.003

    Article  Google Scholar 

  26. P. Sutha, V. Jayanthi, Fetal electrocardiogram extraction and analysis using adaptive noise cancellation and wavelet transformation techniques. J. Med. Syst. 42, 21 (2018). https://doi.org/10.1007/s10916-017-0868-3

    Article  Google Scholar 

  27. N.V. Thakor, Y.-S. Zhu, Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991). https://doi.org/10.1109/10.83591

    Article  Google Scholar 

  28. S. Thongpanja, A. Phinyomark, F. Quaine, Y. Laurillau, C. Limsakul, P. Phukpattaranont, Probability density functions of stationary surface EMG signals in noisy environments. IEEE Trans. Instrum. Meas. 65(7), 1547–1557 (2016). https://doi.org/10.1109/TIM.2016.2534378

    Article  Google Scholar 

  29. M.H. Trabuco, M.V.C. Costa, B. Macchiavello, F.A.D.O. Nascimento, S-EMG signal compression in one-dimensional and two-dimensional approaches. IEEE J. Biomed. Health Inform. 22(4), 1104–1113 (2018). https://doi.org/10.1109/jbhi.2017.2765922

    Article  Google Scholar 

  30. A.R. Verma, Y. Singh, B. Gupta, Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm. Biomed. Eng. Lett. 8, 231–238 (2018). https://doi.org/10.1007/s13534-017-0056-x

    Article  Google Scholar 

  31. S. Yadav, S.K. Saha, R. Kar, D. Mandal, Optimised adaptive noise canceller for denoising cardiovascular signal using SOS algorithm. Biomed. Signal Process. Control 69, 102–830 (2021). https://doi.org/10.1016/j.bspc.2021.102830

    Article  Google Scholar 

  32. S. Yadav, S.K. Saha, R. Kar, D. Mandal, EEG/ERP signal enhancement through an optimally tuned adaptive filter based on marine predators’ algorithm. Biomed. Signal Process. Control 73, 103–427 (2022). https://doi.org/10.1016/j.bspc.2021.103427

    Article  Google Scholar 

  33. Y. Zheng, X. Hu, Interference removal from electromyography based on independent component analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 27(5), 887–894 (2019). https://doi.org/10.1109/tnsre.2019.2910387

    Article  Google Scholar 

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Authors and Affiliations

  1. Electronics and Communication Engineering, National Institute of Technology, Raipur, Chhattisgarh, India

    Shubham Yadav & Suman Kumar Saha

  2. Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Rajib Kar & Durbadal Mandal

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  1. Shubham Yadav
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Yadav, S., Saha, S.K., Kar, R. et al. Noise Confiscation from sEMG Through Enhanced Adaptive Filtering Based on Evolutionary Computing. Circuits Syst Signal Process 42, 4096–4128 (2023). https://doi.org/10.1007/s00034-023-02302-9

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