Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

Abdelouahad Achmamad, Abdelali Belkhou, Atman Jbari


Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy, and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.


Amyotrophic lateral sclerosis, Classification, Electromyography, filter selection, Tunable Q-factor wavelet


Oskarsson, Björn, Tania F. Gendron, and Nathan P. Staff. "Amyotrophic lateral sclerosis: an update for 2018." Mayo Clinic Proceedings. Vol. 93. No. 11. Elsevier, 2018.

Mankowitz, Suzanne KW, ed. Consults in Obstetric Anesthesiology. Springer, 2018.

S. Niedermeyer, M. Murn, and P. J. Choi, “Respiratory Failure in Amyotrophic Lateral Sclerosis,” Chest, no. August, pp. 1–8, 2018.

Z. Dai, Y. Chen, G. Yan, Gang xiao, Z. Shen, and R. Wu, “Progress of magnetic resonance imaging in amyotrophic lateral sclerosis,” Radiol. Infect. Dis., vol. 6, no. 1, pp. 1–7, 2018.

S. Mathis, C. Goizet, A. Soulages, J. M. Vallat, and G. Le Masson, “Genetics of amyotrophic lateral sclerosis: A review,” J. Neurol. Sci., vol. 399, pp. 217–226, 2019.

Chowdhury, Rubana H., et al. "Surface electromyography signal processing and classification techniques." Sensors 13.9 (2013): 12431-12466.

N. Nazmi et al., “A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions,” pp. 1–28.

R. M. Enoka and J. Duchateau, “Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function,” Journal of Applied Physiology, vol. 119, no. 12, pp. 1516-1518, 2015.

Abdelouahad, Achmamad, et al. "Time and frequency parameters of sEMG signal—Force relationship." 2018 4th International Conference on Optimization and Applications (ICOA). IEEE, 2018.

A. Belkhou, A. Jbari, and L. Belarbi, “A continuous wavelet based technique for the analysis of electromyography signals,” Proc. 2017 Int. Conf. Electr. Inf. Technol. ICEIT 2017, vol. 2018-Janua, pp. 1–5, 2018.

Sengar, Namita, Malay Kishore Dutta, and Carlos M. Travieso. "Identification of amyotrophic lateral sclerosis using EMG signals." 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). IEEE, 2017.

K. K. Al-Barazanchi, A. Q. Al-Neami, and A. H. Al-Timemy, “Ensemble of bagged tree classifier for the diagnosis of neuromuscular disorders,” Int. Conf. Adv. Biomed. Eng. ICABME, vol. 2017-Octob, 2017.

S. A. Fattah, A. B. M. S. U. Doulah, and M. Ahmed, “Evaluation of Different Time and Frequency Domain Features of Motor Neuron and Musculoskeletal Diseases,” vol. 43, no. 23, pp. 34–40, 2012.

Doulah, ABM Sayeed Ud, Md Asif Iqbal, and Marzuka Ahmed Jumana. "ALS disease detection in EMG using time-frequency method." 2012 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2012.

E. Gokgoz and A. Subasi, “Biomedical Signal Processing and Control Comparison of decision tree algorithms for EMG signal classification using DWT,” Biomed. Signal Process. Control, vol. 18, pp. 138–144, 2015.

K. PU, A. N, T. S, and B. V, “TQWT Based Features for Classification of ALS and Healthy EMG Signals,” Am. J. Comput. Sci. Inf. Technol., vol. 06, no. 02, pp. 1–7, 2018.

Doulah, ABM Sayeed Ud, et al. "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 8.2 (2014): 155-164.

D. Joshi, A. Tripathi, R. Sharma, and R. B. Pachori, “Computer aided detection of abnormal EMG signals based on tunable-Q wavelet transform,” 2017 4th Int. Conf. Signal Process. Integr. Networks, SPIN 2017, no. November 2018, pp. 544–549, 2017.

A. Sengur, Y. Akbulut, Y. Guo, and V. Bajaj, “Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm,” Heal. Inf. Sci. Syst., vol. 5, no. 1, 2017.

V. K. Mishra, V. Bajaj, A. Kumar, D. Sharma, and G. K. Singh, “International Journal of Electronics and Communications ( AEÜ ) An efficient method for analysis of EMG signals using improved empirical mode decomposition,” AEUE - Int. J. Electron. Commun., vol. 72, pp. 200–209, 2017.

V. K. Mishra, V. Bajaj, A. Kumar, and G. K. Singh, “Analysis of ALS and normal EMG signals based on empirical mode decomposition,” IET Sci. Meas. Technol., vol. 10, no. 8, pp. 963–971, 2016.

F. P. Analysis and A. L. Sclerosis, “Detailed Analysis of Clinical Electromyography Signals To Mia , our lovely daughters Nikoline and Laura and the rest of my family .”

Selesnick, Ivan W. "Wavelet transform with tunable Q-factor." IEEE transactions on signal processing 59.8 (2011): 3560-3575.

H. Lyu, M. Wan, J. Han, R. Liu, and C. Wang, “A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining,” Comput. Biol. Med., vol. 89, pp. 264–274, 2017.

S. J. Lee, Z. Xu, T. Li, and Y. Yang, “A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making,” J. Biomed. Inform., vol. 78, pp. 144–155, 2018.

K. Kira and L. A. Rendell, A Practical Approach to Feature Selection. Morgan Kaufmann Publishers, Inc., 1992.

Y. He, J. Zhou, Y. Lin, and T. Zhu, “A class imbalance-aware Relief algorithm for the classification of tumors using microarray gene expression data,” Comput. Biol. Chem., vol. 80, pp. 121–127, 2019.

X. Huang, A. Maier, J. Hornegger, and J. A. K. Suykens, “Indefinite kernels in least squares support vector machines and principal component analysis,” Appl. Comput. Harmon. Anal., vol. 43, no. 1, pp. 162–172, 2017.

Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.

N. M. N. Mathivanan, N. A. M. Ghani, and R. M. Janor, “Performance analysis of supervised learning models for product title classification,” IAES Int. J. Artif. Intell., vol. 8, no. 3, pp. 299–306, 2019.

W. Feng, H. Sui, J. Tu, W. Huang, and K. Sun, “A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images,” Int. J. Remote Sens., vol. 39, no. 22, pp. 7998–8021, 2018.

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