Prediction and Analysis of Muscular Paralysis Disease using DWT and Hybrid Features
Keywords:Features, Daubechies Wavelet, Gradient Boosting, Neural Network, ALS Signal
Genetic advancements have shown that ALS is not a single entity but consists of a collection of syndromes in which the motor neurons degenerate. Together with these multiple genetic etiologies, there is a broad variability in the disease’s clinical manifestations in terms of the age of symptom onset, site of onset, rate and pattern of progression, and cognitive involvement. In this paper, prediction of human paralysis is done based on extraction of features from the ALS dataset samples. The classification has been carried by two different Machine Learning based algorithms i.e., Gradient Boosting (GB), and neural Network (NN). The standard data set such as ALS has been used for this 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 GB and NN perform better with an accuracy of 98%. Based on the desired accuracy, this classification model serves better compared with existing models.
V. Kehri, R. Ingle, R. Awale and S. Oimbe, “Techniques of EMG signal analysis and classification of neuromuscular diseases,” ICCASP/ICMMD-2016, Advances in Intelligent Systems Research, Vol. 137, pp. 485-491.
Ercan Gokgoz and Abdulhamit Subasi, “Comparison of decision tree algorithms for EMG signal classification using DWT,” Biomedical Signal Processing and Control, Vol. 18, pp. 138-144, April 2015.
R. Jonathan, A. Torres-Castillo, B.Carlos Omar Lopez-Lopez and Miguel A. Padilla-Castaneda, “Neuromuscular disorders detection through time-frequency analysis and classification of Multimuscular EMG signals using Hilbert-Huang transform,” Vol. 71, Part A, No. 103037, pp. 1-14, January 2022.
Abdelouahad Achmamad and Atman Jbari, “A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform,” Bulletin of Electrical Engineering and Informatics, Vol. 9, No. 4, pp. 1420-1429, August 2020, ISSN: 2302-9285, DOI: 10.11591/eei. v9i4.2381.
Marie-Françoise Lucas, Adrien Gaufriau, Sylvain Pascual, Christian Doncarli and Dario Farina, “Multi-channel surface EMG classification using support vector machines and Signal based wavelet optimization,” Biomedical Signal Processing and Control, Vol. 3, No. 2, pp. 169-174, 2008, ISSN 1746-8094, [Online]. Available: https://doi.org/10.1016/j.bspc.2007.09.002.
Abdulhamit Subasi, “Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines,” Computers in Biology and Medicine, Vol. 42, No. 8, pp. 806-815, 2012, ISSN 0010-4825.
Tanu Sharma and Karan Veer, “EMG classification using wavelet functions to determine muscle contraction,” Journal of Medical Engineering & Technology, Vol. 40, No. 3, pp. 9-105, 2016, Taylor & Francis, DOI: 10.3109/03091902.2016.1139202
A. B. M. S. U. Doulah, S. A. Fattah, W. Zhu and M. O. Ahmad, “Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 8, No. 2, pp. 155-164, April 2014, DOI: 10.1109/TBCAS.2014.2309252.
V. Karan, “Wavelet Transform-Based Classification of Electro myogram Signals Using an Anova Technique,” Neurophysiology, Vol. 47, pp.302-309, 2015.
A. Phinyomark, C. Limsakul and P. Phukpattaranont, “Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification,” Measurement Science Review, Vol. 11, No. 2, pp. 45-52, Jan 2011.
A. Benazzouz, R. Guilal, F. Amirouche and Z. E. Hadj Slimane, “EMG Feature Selection for Diagnosis of Neuromuscular Disorders,” 2019 International Conference on Networking and Advanced Systems (ICNAS), pp. 1-5, 2019, DOI: 10.1109/ICNAS.2019.8807862.
M. R. Ahsan, M. I. Ibrahimy and O. O. Khalifa, “The Use of Artificial Neural Network in the Classification of EMG Signals,” 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, pp. 225-229, 2012, DOI: 10.1109/MUSIC.2012.46.
L. Zhang, Y. Shi, W. Wang, Y. Chu, X. Yuan, “Real-time and user-independent feature classification of forearm using EMG signals,” J. Soc. Inf. Disp., Vol. 27, No. 2, pp. 101-107, 2019.
T. Kamali and D. W. Stashuk, “Transparent electrophysiological muscle classification From EMG signals using fuzzy-based multiple instance learning,” IEEE Transac Neural Syst Rehab Eng, Vol. 28, No. 4, pp. 842-849, 2020.
J. Chen, X. Zhang, Y. Cheng and N. Xi, “Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks,” Biomed Signal Process Control, Vol. 40, pp. 335-342, 2018.
C. S. Pattichis, C. N. Schizas and L. Mittleton, “Neural network models in EMG diagnosis”, IEEE Trans. Biomed. Eng, Vol. 42, pp. 486-496, 1995.
C. S. Pattichis and A. G. Elia, “Autoregressive and cepstral analyses of motor unit action potentials”, Med. Eng. Phys, Vol. 21, pp. 405-419, 2000.
C. S. Pattichis and M. S. Pattichis, “Time-scale analysis of motor unit action potentials”, IEEE Trans. Biomed. Eng., Vol. 46, No. 11, pp. 1320-1329, 2000.
A. Subasi, M. Yilmaz and H. R. Ozcalik, “Classification of EMG signals using wavelet neural network,” J. Neurosci. Methods, Vol. 156, 360-367, 2006.
A. Subasi and M. K. Kiymik, “Muscle fatigue detection in EMG using time- frequency methods, ICA and neural networks”, J. Med. Syst., Vol. 34, No. 4, pp. 777-785, 2010.