Have a personal or library account? Click to login
Application of the continuous wavelet transform for the analysis of pathological severity degree of electromyograms (EMGs) signals Cover

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

Open Access
|Sep 2020

References

  1. 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. 2. Merchut MP. Neuropathy, Myopathy, and motor neuron disease. 2011.
  3. 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):1136-1146.10.11591/ijeecs.v11.i3.pp1136-1146
  4. 4. Mishra B, Wadhwani AK, Singh S. EMG Signal Classification for Neuromuscular Disorder using Soft-Computing Techniques. IJIRMPS. 2019;7(1):24-27.
  5. 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; 2012.10.5772/50639
  6. 6. Raez MB, Hussain MS, Mohd-Yasin F. Techniques of EMG Signal Analysis: Detection, Processing, Classification and Applications. Biol Proced Online. 2006;8:11-35.10.1251/bpo115145547916799694
  7. 7. Phinyomark A, Phukpattaranont P, Limsakul C. Feature Reduction and Selection for EMG Signal Classification. Expert Systems with Applications. 2012;39(8):7420-7431.10.1016/j.eswa.2012.01.102
  8. 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;65.10.1007/978-981-10-5122-7_124
  9. 9. Farge M. Wavelet Transforms and their Applications to Turbulence. Ann Rev Fluid Mech. 1992;24:395-457.10.1146/annurev.fl.24.010192.002143
  10. 10. Strazza A, Verdini F, Burattini L, et al. A Time-Frequency Approach for the Assessment of Dynamic Muscle Co-contractions. IFMBE Proceedings. 2019;68/2:223-22610.1007/978-981-10-9038-7_41
  11. 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. 12. Kamaruddin NA, Khalid PI, Shaameri AZ. The Use of Surface Electromyography in Muscle Fatigue Assessments - A Review. Journal of Technology. 2015;74(6):119-124.10.11113/jt.v74.4676
  13. 13. Christodoulou CI, Pattichis CS. Unsupervided Pattern Recognition for the Classification of EMG Signals. IEEE TransBiomed Eng. 199;46(2):169-178.10.1109/10.7408799932338
DOI: https://doi.org/10.2478/pjmpe-2020-0017 | Journal eISSN: 1898-0309 | Journal ISSN: 1425-4689
Language: English
Page range: 149 - 154
Submitted on: Mar 31, 2020
|
Accepted on: Jun 6, 2020
|
Published on: Sep 29, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Aicha Mokdad, Sidi Mohammed El Amine Debbal, Fadia Meziani, published by Polish Society of Medical Physics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.