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EMG Signal Classification Using Discrete Wavelet Transform and Rotation Forest

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CMBEBIH 2019 (CMBEBIH 2019)

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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%.

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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. 2018/10.2-44 h, Jeddah, Saudi Arabia.

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Authors and Affiliations

  1. College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia

    Abdulhamit Subasi

  2. International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Emine Yaman

Authors
  1. Abdulhamit Subasi
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  2. Emine Yaman
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Corresponding author

Correspondence to Abdulhamit Subasi .

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Editors and Affiliations

  1. International Burch University, Sarajevo, Bosnia and Herzegovina

    Almir Badnjevic

  2. Faculty of Medicine, University of Banja Luka, Banja Luka, Bosnia and Herzegovina

    Ranko Škrbić

  3. International Burch University, Sarajevo, Bosnia and Herzegovina

    Lejla Gurbeta Pokvić

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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

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  • DOI: https://doi.org/10.1007/978-3-030-17971-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17970-0

  • Online ISBN: 978-3-030-17971-7

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