Abstract
Electromyographic (EMG) signals are used for the diagnosis of neuromuscular disorders. We have used machine learning algorithms in diagnosing neuromuscular disorders as a decision support system. Hence, in this study, for feature extraction DWT has been used and the Rotation Forest ensemble classifier has been used for classification. Furthermore, we also investigated the performance of different classifiers with Rotation Forest. The performance of a classifier is enhanced using the Rotation Forest ensemble classifier. Significant amount of performance improvement was achieved with a combination of Discrete Wavelet Transform (DWT), and Rotation Forest using k-fold cross validation. Experimental results show the feasibility of Rotation Forest, and we also derive some valuable conclusions on the performance of ensemble learning methods for diagnosis of neuromuscular disorders. Results are promising and showed that the ANN with Random Forest ensemble method achieved an accuracy of 99.13%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Subasi, A.: Classification of EMG signals using combined features and soft computing techniques. Appl. Soft Comput. 12(8), 2188–2198 (2012)
Subasi, A.: Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43(5), 576–586 (2013)
Begg, R., Lai, D.T., Palaniswami, M.: Computational Intelligence in Biomedical Engineering. CRC Press (2008)
Subasi, A., Yilmaz, M., Ozcalik, H.R.: Classification of EMG signals using wavelet neural network. J. Neurosci. Methods 156(1), 360–367 (2006)
Bozkurt, M.R., Subasi, A., Koklukaya, E., Yilmaz, M.: Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models. Turk. J. Electr. Eng. Comput. Sci. 24(3), 1547–1559 (2016)
Gokgoz, E., Subasi, A.: Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders. J. Med. Syst. 38(4), 31 (2014)
Gokgoz, E., Subasi, A.: Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)
Subasi, A.: Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines. Comput. Biol. Med. 42(8), 806–815 (2012)
Subasi, A.: A decision support system for diagnosis of neuromuscular disorders using DWT and evolutionary support vector machines. Signal Image Video Process. 9(2), 399–408 (2015)
Kamali, T., Boostani, R., Parsaei, H.: A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 22(1), 191–200 (2014)
Katsis, C.D., Exarchos, T.P., Papaloukas, C., Goletsis, Y., Fotiadis, D.I., Sarmas, I.: A two-stage method for MUAP classification based on EMG decomposition. Comput. Biol. Med. 37(9), 1232–1240 (2007)
Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012)
Rasheed, S., Stashuk, D., Kamel, M.: A software package for interactive motor unit potential classification using fuzzy k-NN classifier. Comput. Methods Programs Biomed. 89(1), 56–71 (2008)
Dobrowolski, A.P., Wierzbowski, M., Tomczykiewicz, K.: Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders. Comput. Methods Programs Biomed. 107(3), 393–403 (2012)
Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)
Liu, M., Wang, M., Wang, J., Li, D.: Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sens. Actuators B Chem. 177, 970–980 (2013)
Vetterli, M., Herley, C.: Wavelets and filter banks: Theory and design. IEEE Trans. Signal Process. 40(9), 2207–2232 (1992)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5), 961–1005 (1990)
Rioul, O., Vetterli, M.: Wavelets and signal processing. IEEE Signal Process. Mag., 8(LCAV-ARTICLE-1991-005), 14–38 (1991)
Thakor, N.V., Gramatikov, B., Sherman, D.: Wavelet (time-scale) analysis in biomedical signal processing. In: Bronzino, J.D. (ed.) The Biomedical Engineering Handbook, vol. 56, 2nd edn, pp. 1–56. CRC Press LLC, Boca Raton, Florida (2000)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Hall, M., Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Kaufmann Burlingt (2011)
Kalmegh, S.: Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news. Int. J. Innov. Sci. Eng. Technol. 2(2), 438–446 (2015)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Presented at the Australasian joint conference on artificial intelligence, pp. 1015–1021, 2006
Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the kappa statistic. Fam. Med. 37(5), 360–363 (2005)
Lantz, C.A., Nebenzahl, E.: Behavior and interpretation of the κ statistic: Resolution of the two paradoxes. J. Clin. Epidemiol. 49(4), 431–434 (1996)
Yang, Z., Zhou, M.: Kappa statistic for clustered physician–patients polytomous data. Comput. Stat. Data Anal. 87, 1–17 (2015)
Abdullah, A.A., Subasi, A., Qaisar, S.M.: Surface EMG signal classification by using WPD and ensemble tree classifiers. Presented at the CMBEBIH 2017: proceedings of the international conference on medical and biological engineering 2017, vol. 62, p. 475, 2017
Subasi, A., Alharbi, L., Madani, R., Qaisar, S.M.: Surface EMG based classification of basic hand movements using rotation forest. Presented at the advances in science and engineering technology international conferences (ASET), 2018, pp. 1–5, 2018
Acknowledgements
The author would like to extend many thanks to Dr. Mustafa Yilmaz at University of Gaziantep, Neurology Department for providing the EMG data used in this research.
Funding This work was supported by Effat University with the Decision Number of UC#7/28 Feb. 2018/10.2-44Â h, Jeddah, Saudi Arabia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Subasi, A., Yaman, E. (2020). EMG Signal Classification Using Discrete Wavelet Transform and Rotation Forest. In: Badnjevic, A., Škrbić, R., Gurbeta Pokvić, L. (eds) CMBEBIH 2019. CMBEBIH 2019. IFMBE Proceedings, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-17971-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-17971-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17970-0
Online ISBN: 978-3-030-17971-7
eBook Packages: EngineeringEngineering (R0)