Performance Analysis of Muscular Paralysis Disease Using Machine Learning
DOI:
https://doi.org/10.51983/ajcst-2022.11.1.3242Keywords:
Electromyography, Wavelet Transform, Random Forest, Multilayer Perceptron, MayosignalAbstract
Electromyography has been used for many years in regulating paralyzed limb. Captured and Processed EMG is an indication of human movements. EMG signal (called as Mayo signal) will be recorded by surface electrodes and needle electrodes. In this work, the combined time and frequency analysis has been carried out to extract the required features using Wavelet Transform tools. Further the classification has been carried by 2 different Machine Learning based algorithms, Random Forest (RF), and Multilayer Perceptron (MLP). The standard data set has been used for the purpose. The classifier model has used 80% data as a training set and the remaining 20% of data as the test set. The result shows that Random Forest and MLP perform better with an accuracy of 98 %. This classification model can serves as a promising candidate for analysis of muscular paralysis.
References
K. Mahaphonchaikul, D. Sueaseenak, C. Pintavirooj, M. Sangworasil, and S. Tungjitkusolmun, "EMG signal feature extraction based on wavelet transform," in IEEE International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 327-331, 2010.
F. A. Mahdavi, S. A. Ahmad, M. H. Marhaban, and M. R. Akbarzadeh, "Surface Electromyography Feature Extraction Based on Wavelet Transform," in International Journal of Integrated Engineering, vol. 4, no. 3, pp. 1-7, 2012.
I. Elamvazuthi, G. A. Ling, K. A. R. K. Nurhanim, P. Vasant, and S. Parasuraman, "Surface electromyography (sEMG) feature extraction based on Daubechies wavelets," in Procedia Computer Science, IEEE, vol. 76, pp. 229-234, 2015.
M. I. Ibrahimy, M. R. Ahsan, and O. O. Khalifa, "Design and Performance Analysis of Artificial Neural Network for Hand Motion Detection from EMG Signals," in World Applied Sciences Journal, vol. 23, no. 6, pp. 751-758, 2013.
A.-C. Tsai, T.-H. Hsieh, J.-J. Luh, and T. Lin, "A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions," in Elsevier Journal on Biomedical Signal Processing and Control, vol. 11, pp. 17–26, 2014.
A. Subasi, E. Yaman, Y. Somaily, H. A. Alynabawi, F. Alobaidi, and S. Altheibani, "Automated EMG Signal Classification for Diagnosis of Neuromuscular Disorders Using DWT and Bagging," in Elsevier Procedia Computer Science, vol. 140, pp. 230-237, 2018.
V. A. Krishna and P. Thomas, "Classification of EMG Signals Using Spectral Features Extracted from Dominant Motor Unit Action Potential," in International Journal of Engineering and Advanced Technology, vol. 4, no. 5, pp. 196-200, 2015.
A. L. D. Siqueira and J. A. B. Soares, "A novel method for EMG decomposition based on matched filters," in Research on Biomedical Engineering, vol. 31, no. 1, pp. 44-55, 2015.
X. Ren, C. Zhang, X. Li, G. Yang, T. Potter, and Y. Zhang, "Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution," in Frontiers in Neurology, vol. 9, no. 2, pp. 1-9, 2018.
P. Geethanjali, "Comparative study of PCA in classification of multichannel EMG signals," in Australasian Physical & Engineering Sciences in Medicine, vol. 38, pp. 331-343, 2015.
R. Begg, D. T. H. Lai, and M. Palaniswami, "Computational Intelligence in Biomedical Engineering," in CRC Press Taylor & Francis Group, Boca Raton, vol. 4, no. 2, pp. 1-10, 2008.
A. Subasi, "Classification of EMG signals using combined features and soft computing techniques," in Appl. Soft Computing, vol. 12, pp. 2188-2198, 2012.
M. Hassoun, C. Wang, and A. Spitzer, "NNERVE: neural network extraction of repetitive vectors for electromyography," in IEEE Trans. Biomed. Eng, vol. 41, no. 11, pp. 1053-1061, 2016.
C. N. Schizas, C. S. Pattichis, I. Schofield, P. Fawcett, and L. Middleton, "Artificial neural nets in computer-aided macro motor unit potential classification," in IEEE Eng. Med. Biol. Mag., vol. 9, no. 3, pp. 31-38, 2012.
C. N. Schizas, C. S. Pattichis, and L. Middleton, "Neural networks, genetic algorithms, and the k-means algorithm: in search of data classification," in: Proceedings of the International Workshop on Combinations of Genetic Algorithms and Neural Networks, vol. 1, pp. 201-222, 1992.
C. S. Pattichis and A. G. Elia, "Autoregressive and cepstral analyses of motor unit action potentials," in Med. Eng. Phys, vol. 21, pp. 405-419, 2000.
C. S. Pattichis and M. S. Pattichis, "Time-scale analysis of motor unit action potentials," in IEEE Trans. Biomed. Eng., vol. 46, no. 11, pp. 1320-1329, 2000.
A. Subasi and M. K. Kiymik, "Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks," in J. Med. Syst., vol. 34, no. 4, pp. 777-785, 2010.
J. Breckling, Ed., "The Analysis of Directional Time Series: Applications to Wind Speed and Direction," ser. Lecture Notes in Statistics, Berlin, Germany: Springer, vol. 61, 1989.
S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, "A novel ultrathin elevated channel low-temperature poly-Si TFT," in IEEE Electron Device Lett., vol. 20, pp. 569-571, Nov. 1999.
M. Wegmuller, J. P. Von Der Weid, P. Oberson, and N. Gisin, "High resolution fiber distributed measurements with coherent OFDR," in Proc. ECOC’00, 2000, paper 11.3.4, pp. 109.
R. E. Sorace, V. S. Reinhardt, and S. A. Vaughn, "High-speed digital-to-RF converter," U.S. Patent 5 668 842, Sep. 16, 1997.
The IEEE website, 2007. [Online]. Available: http://www.ieee.org/.
M. Shell. IEEE tran webpage on CTAN, 2007. [Online]. Available: http://www.ctan.org/tex-archive/macros/latex/contrib/IEEEtran/.
FLEXChip Signal Processor (MC68175/D), Motorola, 1996.
"PDCA12-70 data sheet," Opto Speed SA, Mezzovico, Switzerland.
A. Karnik, "Performance of TCP congestion control with rate feedback: TCP/ABR and rate adaptive TCP/IP," M. Eng. thesis, Indian Institute of Science, Bangalore, India, Jan. 1999.
J. Padhye, V. Firoiu, and D. Towsley, "A stochastic model of TCP Reno congestion avoidance and control," Univ. of Massachusetts, Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.
A. M. Abdullah, "Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification," IEEE Std. 802.11, 1997.
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