AccScience Publishing / NSCE / Online First / DOI: 10.36922/NSCE026140011
Submit to NSCE
Cite this article
57
Views
Related Info Links
Google Scholar
More by Authors Links
Jean de Dieu Nkapkop
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
RESEARCH ARTICLE

Frequency- and bit-rate-adaptive compression of electromyography signals by wavelet packet transform and optimal scalar quantizer bit rate allocation

Joseph Mvogo Ngono1 ,  Arnaud Nanfak2 ,  Jean de Dieu Nkapkop3* ,  Auguste Noumsi Woguia1 ,  Christophe Lessouga Etoundi3 ,  Christophe Lessouga Etoundi3 ,  Zeric Tabekoueng Njitacke4 ,  Pierre Ele5
Show Less
1 Applied Computing Laboratory, Faculty of Science, University of Douala, Douala, Littoral Region, Cameroon
2 Laboratory of Electronic, Electrical Engineering, Automation and Telecommunication, National Higher Polytechnic School of Douala, University of Douala, Douala, Littoral Region, Cameroon
3 Technology and Applied Sciences Laboratory, University Institute of Technology, University of Douala, Douala, Littoral Region, Cameroon
4 Department of Electrical and Electronic Engineering, College of Technology, University of Buea, Buea, Littoral Region, Cameroon
5 Laboratory of Electrical Engineering, Mechatronic and Signal Treatment, National Advanced School of Engineering, University of Yaound´e 1, Yaound´e, Centre Region, Cameroon
NSCE, 026140011 https://doi.org/10.36922/NSCE026140011
Received: 30 March 2026 | Revised: 28 April 2026 | Accepted: 18 May 2026 | Published online: 23 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
XML
Cite
Abstract

Electromyography (EMG) signals are widely used for neuromuscular assessment, rehabilitation, and prosthetic control, but their high sampling rates and long recording durations generate large data volumes. This study presents a frequency- and bit-rate-adaptive compression method for EMG signals based on wavelet packet transform and optimal scalar quantizer bit allocation. The method first decomposed the signal using a complete wavelet packet tree of depth L = 5. Then, the coefficients of each sub-band were modeled as generalized Gaussian distributions, and analytical rate-distortion estimates were derived for a uniform scalar quantizer. These estimates were used in a Lagrangian optimization framework to jointly select the optimal wavelet packet basis and allocate quantization bit rates to achieve the target compression ratio. Experimentswere conducted on four EMG records, including two surface EMG signals and two needle EMG signals, using Daubechies db2 and biorthogonal bior4.4 wavelets over compression ratios from 5 to 25. The proposed method was compared with a conventional wavelet-based baseline using root-meansquare error (RMSE), signal-to-noise ratio (SNR), percentage root-mean-square difference (PRD), and quality score (QS). Across all tested operating points, the optimal adaptive configuration reduced RMSE and PRD by 37.7% on average, increased SNR by 4.33 dB, and improved QS by 69.5% compared with the optimal baseline configuration. These results highlight the effectiveness of the proposed adaptive framework, making it well-suited for real-time biomedical signal compression and telemedicine applications.

Keywords
Frequency- and bit-rate-adaptive compression
Electromyography signal
Wavelet packet transform
Scalar quantizer bit rates
Funding
None.
Conflict of interest
The authors declare no conflict of interest.
References
  1. Biagetti G, Crippa P, Falaschetti L, Mansour A, Turchetti C. Energy and performance analysis of lossless compression algorithms for wireless EMG sensors. Sensors. 2021;21(15):5160. https://doi.org./10.3390/s21155160
  2. Gupta A, Bansal A, Khanduja V. Modern lossless compression techniques: review, comparison and analysis. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE; 2017:1-8. https://doi.org./10.1109/ICECCT.2017.8117850
  3. Ntsama PE, Welba C, Ele P. Comparison study of EMG signals compression by methods transform using vector quantization, SPIHT and arithmetic coding. SpringerPlus. 2016;5:444.https://doi.org./10.1186/s40064-016-2095-7
  4. Elakkiya S, Thivya KS. Comprehensive review on lossy and lossless compression techniques. J Inst Eng India Ser B. 2022;103:1003-1012. https://doi.org./10.1007/s40031-021-00686-3
  5. Subasi A. Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput. 2012;12(8):2188-2198. https://doi.org./10.1016/j.asoc.2012.03.035
  6. Arias Correa JD, Roschildt Pinto AS, Montez C. Lossy data compression for IoT sensors: a review. Internet Things. 2022;19:100516. https://doi.org./10.1016/j.iot.2022.100516
  7. Wang K, Bai Y, Li D, Zhai D, Jiang J, Liu X. Learning lossless compression for high bit-depth volumetric medical images. IEEE Trans Image Process. 2025;34:113-125. https://doi.org./10.1109/TIP.2024.3513156
  8. Shuvo SB, Alam SS, Ayman SU, et al. Application of wavelet transformation and artificial intelligence techniques in healthcare: a systematic review. WIREs Data Min Knowl Discov. 2025;15:e70007. https://doi.org./10.1002/widm.70007
  9. Gao J, Wang B, Wang Z, Wang Y, Kong F. A wavelet transform based image segmentation method. Optik. 2020;208:164123. https://doi.org./10.1016/j.ijleo.2019.164123
  10. Legendre S, Massicotte D, Goyette J, Bose TK. Wavelettransform-based method of analysis for Lamb-wave ultrasonic NDE signals. IEEE Trans Instrum Meas. 2000;49(3):524-530. https://doi.org./10.1109/19.850388
  11. Lal B, Ajbani M, Sodhro AH, Gravina R, O’Keeffe D. Efficient ECG compression and reconstruction via deep compressive sensing and hardware-efficient models. Array. 2026;30:100791. https://doi.org./10.1016/j.array.2026.100791
  12. Benitez VH, Pacheco J, Cuamea-Cruz G. Lightweight deep learning for sEMG-based finger position classification and embedded system deployment. IEEE Access. 2025;13:38181-38194. https://doi.org./10.1109/ACCESS.2025.3545827
  13. Sim˜ao M, Neto P, Gibaru O. EMG-based online classification of gestures with recurrent neural networks. Pattern Recognit Lett. 2019;128:45-51. https://doi.org./10.1016/j.patrec.2019.07.021
  14. Hilton ML. Wavelet and wavelet packet compression of electrocardiograms. IEEE Trans Biomed Eng. 1997;44(5):394-402. https://doi.org./10.1109/10.568915
  15. Pooyan M, Taheri A, Moazami-Goudarzi M, Saboori I. Wavelet compression of ECG signals using SPIHT algorithm. Int J Med Health Sci. 2007;1(2):85-88. https://doi.org./10.5281/zenodo.1072022
  16. Dasan E, Gnanaraj R. Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach. Circuits Syst Signal Process. 2022;41(11):6152-6181. https://doi.org./10.1007/s00034-022-02071-x
  17. Du X, Liang K, Lv Y, Qiu S. Fast reconstruction of EEG signal compression sensing based on deep learning. Sci Rep. 2024;14:5087. https://doi.org./10.1038/s41598-024-55334-9
  18. Rhif M, Ben Abbes A, Farah IR, Mart´ınez B, Sang Y. Wavelet transform application for/in non-stationary time-series analysis: a review. Appl Sci. 2019;9(7):1345. https://doi.org./10.3390/app9071345
  19. Phinyomark A, Limsakul C, Phukpattaranont P. Application of wavelet analysis in EMG feature extraction for pattern classification. Meas Sci Rev. 2011;11(2):45-52. https://doi.org./10.2478/v10048-011-0009-y
  20. Sitole SP, Sup FC. Continuous prediction of human joint mechanics using EMG signals: a review of model-based and model-free approaches. IEEE Trans Med Robot Bionics. 2023;5(3):528-546. https://doi.org./10.1109/TMRB.2023.3292451
  21. Ramchandran K, Vetterli M. Best wavelet packet bases in a ratedistortion sense. IEEE Trans Image Process. 1993;2(2):160-175. https://doi.org./10.1109/83.217221
  22. Kaaniche M, Fraysse A, Pesquet-Popescu B, Pesquet JC. A convex programming bit allocation method for sparse sources. In: 2012 Picture Coding Symposium. IEEE; 2012:277-280. https://doi.org./10.1109/PCS.2012.6213346
  23. Golilarz NA, Dehzangi I, Rahimi K. Optimized adaptive based method for MR image denoising. In: 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE; 2024:976-983. https://doi.org./10.1109/CICN63059.2024.10847356
  24. Trabuco MH, Costa MVC, Nascimento FAO. S EMG signal compression based on domain transformation and spectral shape dynamic bit allocation. Biomed Eng Online. 2014;13:22. https://doi.org./10.1186/1475-925X-13-22
  25. Subasi A, Subasi ME, Qaisar SM. EEG-based biometric authentication using wavelet packet decomposition and ensemble classifiers. In: Naik GR, editor. Time-Frequency Analysis in Biomedical Engineering. 1st ed. CRC Press; 2026. https://doi.org./10.1201/9781003611448-17
  26. Erkoc ME. A deep learning based compressed sensing method for electrocardiogram signals. Circuits Syst Signal Process. Published online 2026. https://doi.org./10.1007/s00034-026-03506-5
  27. Piyathilaka L, Sul J, Arachchige SNSD. Advances in EMG signal processing and pattern recognition: techniques, challenges, and emerging applications. Electronics. 2026;15(3):590. https://doi.org./10.3390/electronics15030590
  28. Anwar A, Khalifa Y, Coyle JL, Sejdic E. Transformers in biosignal analysis: a review. Inf Fusion. 2025;114:102697. https://doi.org./10.1016/j.inffus.2024.102697
  29. Shin J, Miah ASM, Kabir MH, Rahim MA, Al Shiam A. A methodological and structural review of hand gesture recognition across diverse data modalities. IEEE Access. 2024;12:142606-142639. https://doi.org./10.1109/ACCESS.2024.3456436
  30. Coifman RR,Wickerhauser MV. Entropy-based algorithms for best basis selection. IEEE Trans Inf Theory. 1992;38(2):713-718. https://doi.org./10.1109/18.119732
  31. Meyer FG, Averbuch A, Str ¨omberg JO. Fast adaptive wavelet packet image compression. IEEE Trans Image Process. 2000;9(5):792-800. https://doi.org./10.1109/83.841526
  32. Al-Fahoum AS, Al-Fraihat AA. Methods of EEG signal features extraction using linear analysis in frequency and timefrequency domains. Int Sch Res Notices. 2014;2014:730218. https://doi.org./10.1155/2014/730218
  33. Ma J, Bao X, Qin F, et al. CNN-MSTINet: a lightweight hybrid network with multi-scale temporal interaction for sparse sEMG-based gesture recognition. Meas Sci Technol. 2026;37(7):076111. https://doi.org./10.1088/1361-6501/ae44bc
  34. Fraysse A, Pesquet-Popescu B, Pesquet J. On the uniform quantization of a class of sparse sources. IEEE Trans Inf Theory. 2009;55(7):3243-3263. https://doi.org./10.1109/TIT.2009.2021329
  35. Atzori M, Gijsberts A, Heynen S, et al. Building the NinaPro database: a resource for the biorobotics community. In: 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE; 2012:1258-1265. https://doi.org./10.1109/BioRob.2012.6290287
  36. Howard PG, Vitter JS. Analysis of arithmetic coding for data compression. Inf Process Manag. 1992;28(6):749-763. https://doi.org./10.1016/0306-4573(92)90066-9
  37. Yan T, Liang S, Mu L, et al. Adaptive Myo-spatial correction for overcoming electrode shifts impact in sEMG-based gesture recognition. Biomed Signal Process Control. 2025;103:107356. https://doi.org./10.1016/j.bspc.2024.107356
  38. Li X, Zhang X, Zhang L, Chen X, Zhou P. A transformer-based multi-task learning framework for myoelectric pattern recognition supporting muscle force estimation. IEEE Trans Neural Syst Rehabil Eng. 2023;31:3255-3264. https://doi.org./10.1109/TNSRE.2023.3298797
  39. Ji R, Tan K, Wang X, et al. PatchOut: a novel patch-free approach based on a transformer-CNN hybrid framework for finegrained land-cover classification on large-scale airborne hyperspectral images. Int J Appl Earth Obs Geoinf. 2025;138:104457. https://doi.org./10.1016/j.jag.2025.104457
  40. Guo W, Fouda ME, Eltawil AM, Salama KN. Neural coding in spiking neural networks: a comparative study for robust neuromorphic systems. Front Neurosci. 2021;15:638474. https://doi.org./10.3389/fnins.2021.638474
Next article in this issue
Share
Back to top
Nonlinear Science and Control Engineering, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept