Advertisement

Feature Extraction and Classification Between Control and Parkinson’s Using EMG Signal

  • Roselene Subba
  • Akash Kumar BhoiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)

Abstract

The main objective of the proposed work is to classify and differentiate Parkinson’s disease from other neuromuscular disease with the help of Electromyogram (EMG) signal. An Electromyogram signal detects the electric potential generated by muscle cells when these muscle cells contract or relax. However, these electromyography signal itself failed to differentiate between these neuromuscular diseases as their symptoms are almost the same. Therefore, certain features were examined and studied like average distance peaks, discrete wavelet functions, entropy, band power, peak-magnitude-to-RMS ratio, mean complex cepstrum and maximum value of single-sided amplitude, etc. These features were extracted, and with these features, we can differentiate between these neuromuscular diseases including Parkinson’s disease. Two classifiers were used for detection and classification of Parkinson’s, they were Decision Tree and Naive Bayes. However, the accuracy in Decision Tree was found out to be 88.38%, while the accuracy in Naive Bayes was found out to be 54.07%.

Keywords

Electromyogram Parkinson’s disease Feature extraction Decision Tree Naive Bayes Classifier 

References

  1. 1.
    Rissanen, S.M., Kankaanpää, M., Meigal, A., Tarvainen, M.P., Nuutinen, J., Tarkka, I.M., Airaksinen, O., Karjalainen, P.A.: Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis. Med. Biol. Eng. Comput. 46(9), 849–858 (2008)CrossRefGoogle Scholar
  2. 2.
    Rissanen, S., Kankaanpää, M., Tarvainen, M.P., Nuutinen, J., Tarkka, I.M., Airaksinen, O., Karjalainen, P.A.: Analysis of surface EMG signal morphology in Parkinson’s disease. Physiol. Meas. 28(12), 1507 (2007)CrossRefGoogle Scholar
  3. 3.
    Kugler, P., Jaremenko, C., Schlachetzki, J., Winkler, J., Klucken, J., Eskofier, B.: Automatic recognition of Parkinson’s disease using surface electromyography during standardized gait tests. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5781–5784. IEEE (2013, July)Google Scholar
  4. 4.
    Putri, F.T., Caesarendra, W., Ariyanto, M., Pasmanasari, E.D.: Electromyography gait test for Parkinson disease recognition using artificial neural network classification in Indonesia. Majalah Ilmiah Momentum 12(2) (2016)Google Scholar
  5. 5.
    Jeon, H., Lee, W., Park, H., Lee, H., Kim, S., Kim, H., Park, K.: Automatic classification of tremor severity in Parkinson’s disease using a wearable device. Sensors 17(9), 2067 (2017)CrossRefGoogle Scholar
  6. 6.
    Muthuraman, M., et al.: A new diagnostic test to distinguish tremulous Parkinson’s disease from advanced essential tremor. Mov. Disord. 26(8), 1548–1552 (2011)CrossRefGoogle Scholar
  7. 7.
    Kugler, P., et al.: Automated classification of Parkinson’s disease and essential tremor by combining electromyography and accelerometer signals. Basal Ganglia 3(1), 61 (2013)CrossRefGoogle Scholar
  8. 8.
    Rissanen, S.M., et al.: Discrimination of EMG and acceleration measurements between patients with Parkinson’s disease and healthy persons. In: 2010 Annual International Conference of the IEEE, Engineering in Medicine and Biology Society EMBC, pp. 4878–4881 (2010)Google Scholar
  9. 9.
    Askari, S., et al.: An EMG-based system for continuous monitoring of clinical efficacy of Parkinson’s disease treatments. In: 2010 Annual International Conference of the IEEE, Engineering in Medicine and Biology Society EMBC, pp. 98–101 (2010)Google Scholar
  10. 10.
    Chowdhury, R., Reaz, M., Ali, M., Bakar, A., Chellappan, K., Chang, T: Surface electromyography signal processing and classification techniques. Sensors 13(9), 12431–12466 (2013)CrossRefGoogle Scholar
  11. 11.
    Bhoi, A.K.: Classification and clustering of Parkinson’s and healthy control gait dynamics using LDA and K-means. Int. J. Bioautomation 21(1) (2017)Google Scholar
  12. 12.
    Hausdorff, J.M., Lertratanakul, A., Cudkowicz, M.E., Peterson, A.L., Kaliton, D., Goldberger, A.L.: Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J. Appl. Physiol. 88, 2045–2053 (2000)CrossRefGoogle Scholar
  13. 13.
    Phung, D.Q., Tran, D., Ma, W., Nguyen, P., Pham, T.: Using Shannon entropy as EEG signal feature for fast person identification. In: ESANN, vol. 4, issue No. 1, pp. 413–418 (2014, April)Google Scholar
  14. 14.
    Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in eeg. Comput. Methods Prog. Biomed. 80(3), 187–194 (2005)CrossRefGoogle Scholar
  15. 15.
    Machado, J., Balbinot, A.: Executed movement using EEG signals through a Naive Bayes classifier. Micromachines 5(4), 1082–1105 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electrical & Electronics Engineering, Sikkim Manipal Institute of TechnologySikkim Manipal UniversityGangtokIndia

Personalised recommendations