Application of the continuous wavelet transform for the analysis of pathological severity degree of electromyograms (EMGs) signals

• Autorzy: Mokdad Aicha , Debbal Sidi Mohammed El Amine , Meziani Fadia

Identyfikatory

DOI

10.2478/pjmpe-2020-0017

• Języki publikacji: EN

Abstrakty

EN

The aim of this work was twofold: first, to propose signal processing methods for assessing the temporal and spectral changes of parameters (mean absolute value, the energy and standard deviation as temporal parameters, total and mean power as frequency parameters) of the surface myoelectric signal of the various patient groups like normal, myopathic and neuropathic during muscles contraction of biceps. Secondly, to analyze this electrical manifestation of neuromuscular disorders by the implementation of time-frequency analysis using continuous wavelet that allows us to qualify this method to evaluate, appreciate the pathology and determine its degree of severity which was unable by extracting mentioned parameters. Our results showed that this approach presents satisfactory performances especially to follow patients with the least severe pathology.

Słowa kluczowe

EN

EMG; parameters; time-frequency; continuous wavelet; severity

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2020
• Tom: Vol. 26, Iss. 3
• Strony: 149--154
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Mokdad Aicha

• Genie Biomedical Laboratory (GBM), Faculty of Technology University of Abou Bekr Belkaid Tlemcen Tlemcen, Algeria

autor

Debbal Sidi Mohammed El Amine

• Genie Biomedical Laboratory (GBM), Faculty of Technology University of Abou Bekr Belkaid Tlemcen Tlemcen, Algeria

autor

Meziani Fadia

• Genie Biomedical Laboratory (GBM), Faculty of Technology University of Abou Bekr Belkaid Tlemcen Tlemcen, Algeria

Bibliografia

• 1. Ruchika SD. An Explanatory Study of the Parameters to be Measured from EMG Signal. Int J Eng Comp Sci. 2013;2(1):207-213.
• 2. Merchut MP. Neuropathy, Myopathy, and motor neuron disease. 2011.
• 3. Tengku Zawawi TNS, Abdullah AR, Jopri MH, et al. A Review of Electromyography Signal Analysis Techniques for Musculoskeletal Disorders. Indonesian J Electrical Eng Comp Sci. 2018;11(3):1136-1146.
• 4. Mishra B, Wadhwani AK, Singh S. EMG Signal Classification for Neuromuscular Disorder using Soft-Computing Techniques. IJIRMPS. 2019;7(1):24-27.
• 5. Phinyomark A, Thongpanja S, Hu H, et al. The Usefulness of Mean and Median Frequencies in Electromyography Analysis. In: Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges. IntechOpen; 2012.
• 6. Raez MB, Hussain MS, Mohd-Yasin F. Techniques of EMG Signal Analysis: Detection, Processing, Classification and Applications. Biol Proced Online. 2006;8:11-35.
• 7. Phinyomark A, Phukpattaranont P, Limsakul C. Feature Reduction and Selection for EMG Signal Classification. Expert Systems with Applications. 2012;39(8):7420-7431.
• 8. Strazza A, Verdini F, Burattini L, et al. Time-frequency Analysis of Surface EMG signals for Maximum Energy Localization during Walking. In: Eskola H., Väisänen O., Viik J., Hyttinen J. (eds) EMBEC & NBC 2017. IFMBE Proceedings. 2018;65.
• 9. Farge M. Wavelet Transforms and their Applications to Turbulence. Ann Rev Fluid Mech. 1992;24:395-457.
• 10. Strazza A, Verdini F, Burattini L, et al. A Time-Frequency Approach for the Assessment of Dynamic Muscle Co-contractions. IFMBE Proceedings. 2019;68/2:223-226
• 11. Ismail AR, Asfour SS. Continuous Wavelet Transform Application to EMG Signals During Human Gait. Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), Pacific Grove, CA, 1998, pp. 325-329.
• 12. Kamaruddin NA, Khalid PI, Shaameri AZ. The Use of Surface Electromyography in Muscle Fatigue Assessments - A Review. Journal of Technology. 2015;74(6):119-124.
• 13. Christodoulou CI, Pattichis CS. Unsupervided Pattern Recognition for the Classification of EMG Signals. IEEE TransBiomed Eng. 199;46(2):169-178.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).