Warianty tytułu
Języki publikacji
Abstrakty
Electromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.
Rocznik
Tom
Strony
155--160
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
- Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria
- Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria, adebbal@yahoo.fr
autor
- Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bekr Belkaid Tlemcen, Tlemcen, Algeria
Bibliografia
- 1. Rissanen SM, Kankaanpaa M, Tarvainen MP, et al. Extraction of Typical Features from Surface EMG Signals in Paarkinson's Disease. 11th International Congress of Parkinsons's Disease and Movement Disorders, Istanbul, Turkey, 2007.
- 2. Rowland LP. Amyotrophic Lateral Sclerosis: Theories and Therapies. Ann. Neurol. 1994;35(2):129-130. doi: 1O.1002/ana.410350202.
- 3. Kaplanis PA, Pattichis CS, Hadjileontiadis LJ, Panas SM. Bispectral analysis ofsurface EMG. In: Proceedings of the 10th Mediterranean Electrotechnical Conference, vol. II. p. 770-773, 2000.
- 4. Nazarpour K, Sharafat AR, Firoozabadi SMP. Application of higher order statisticsto surface electromyogram signal classification. IEEE Trans Biomed Eng. 2007;54(10):1762-1769.
- 5. Meziani F, Rerbal S, Debbal SM. Spectro-temporal analysis of electromyogram signals (EMGs). Int J Med Eng Inf. 2019;22(2).
- 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. Mishra B, Wadhwani AK, Singh S. EMG Signal Classification for Neuromuscular Disorder using Soft-Computing Techniques. IJIRMPS. 2019;7(1):24-27.
- 13. J Too, Abdullah AR, Tengku Zawawi TNS, et al. Classification of EMG Signal Based on Time Domain and Frequency Domain Features International Journal of Human and Technology Interaction. 2017;1(1):25-29.
- 14. Nikolic M. Detailed analysis of clinical electromyography signals: Emg decomposition, findings and firing pattern analysis in controls and patients with myopathy and amytrophic lateral sclerosis. Ph.D. dissertation, 2001. Online. Available: http://www.emglab.net
- 15. Chua KC, Chandran V, Acharyaa UR, Lima CM. Application of higher order statistics/spectra in biomedical signals. Med Eng Phys. 2010;32(7):679-689.
- 16. Nikias CL, Raghuveer MR. Bispectrum estimation a digital signal processing framework. Proceedings of the IEEE. 1987;75(7):869-891.
- 17. Nikias CL, Petropulu AP. Higher-order spectra analysis: a nonlinear Signal Processing Framework. Prentice-Hall, Englewood Cliffs, NJ; 1993.
- 18. Mishra A, Mishra V, Yadav VK. Comparison of normal and pathological gait using EMG signal. Int J Adv Res Sci Eng. 2017;6(2):442-451.
- 19. Cabrera C. Analyse du signal myoélectrique pour l'évaluation de la fatigue périphérique chez des nageurs de haut niveau en demifond. 2014.
- 20. Karthick PA, Ghosh DM, Ramakrishnan S. Surface electromyography-based muscle fatigue detection using high-resolution timefrequency methods and machine learning algorithms. Comput Methods Programs Biomed. 2018;154:45-56.
- 21. Khan S, Hussain M, Aboalsamh H, Bebis G. A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimedia Tools and Applications. 2017;76(1):33-57.
- 22. Benazzouz A, Guilal R, Amirouche F, et al. EMG Feature Selection for Diagnosis of Neuromuscular Disorders. 2019 International Conference on Networking and Advanced Systems (ICNAS). 2019.
- 23. Xing K., Yang P, Huang J, et al. A real-time EMG pattern recognition method for virtual myoelectric hand control. Neurocomputing. 2014;136:345-355.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-c93a62ee-170c-4513-853d-2e996c5c506a