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A Binary Bat Approach for Identification of Fatigue Condition from sEMG Signals

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Book cover Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

In this work, an attempt has been made to investigate the effectiveness of binary bat algorithm as a feature selection method to classify sEMG signals under fatigue and nonfatigue conditions. The sEMG signals are recorded from the biceps brachii muscle of 50 healthy volunteers. The signals are preprocessed and then multiscale Renyi entropy based feature are extracted. The binary bat algorithm is used for feature selection and the effectiveness is compared with information gain based ranker. The performance of the feature selection algorithms are validated by performing classification using Naïve Bayes, and least square support vector machines. The results show a decreasing trend in the multiscale Renyi entropy with increase in scale. Additionally, higher entropy values where observed in fatigue condition. The classification results showed that a maximum accuracy of 86.66 % is obtained with least square SVM and binary bat algorithm. It appears that, this technique is useful in identifying muscle fatigue in varied clinical conditions.

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References

  1. Venugopal, G., Navaneethakrishna, M., Ramakrishnan, S.: Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals. Expert Syst. Appl. 41, 2652–2659 (2014)

    Article  Google Scholar 

  2. Falup-Pecurariu, C.: Fatigue assessment of Parkinson’s disease patient in clinic: specific versus holistic. J. Neural Transm. 120, 577–581 (2013)

    Article  Google Scholar 

  3. de Vries, J.M., Hagemans, M.L.C., Bussmann, J.B.J., van der Ploeg, A.T., van Doorn, P.A.: Fatigue in neuromuscular disorders: focus on Guillain-Barré syndrome and Pompe disease. Cell. Mol. Life Sci. 67, 701–713 (2010)

    Article  Google Scholar 

  4. Greig, C.A., Jones, D.A.: Muscle physiology. Surgery 28, 55–59 (2010)

    Google Scholar 

  5. Ma, L., Chablat, D., Bennis, F., Zhang, W., Hu, B., Guillaume, F.: A novel approach for determining fatigue resistances of different muscle groups in static cases. Int. J. Ind. Ergon. 41, 10–18 (2011)

    Article  Google Scholar 

  6. Thrasher, A., Graham, G.M., Popovic, M.R.: Reducing muscle fatigue due to functional electrical stimulation using random modulation of stimulation parameters. Artif. Organs 29, 453–458 (2005)

    Article  MATH  Google Scholar 

  7. Song, J.-H., Jung, J.-W., Lee, S.-W., Bien, Z.: Robust EMG pattern recognition to muscular fatigue effect for powered wheelchair control. J. Intell. Fuzzy Syst. 20, 3–12 (2009)

    Google Scholar 

  8. Fuglsang-Frederiksen, A.: The role of different EMG methods in evaluating myopathy. Clin. Neurophysiol. 117, 1173–1189 (2006)

    Article  Google Scholar 

  9. Knaflitz, M., Molinari, F.: Assessment of muscle fatigue during biking. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 17–23 (2003)

    Article  Google Scholar 

  10. Soylu, A.R., Arpinar-Avsar, P.: Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction. J. Electromyogr. Kinesiol. 20, 773–776 (2010)

    Article  Google Scholar 

  11. Kumar, D.K., Arjunan, S.P., Naik, G.R.: Measuring increase in synchronization to identify muscle endurance limit. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 578–587 (2011)

    Article  Google Scholar 

  12. Dimitrov, G.V., Arabadzhiev, T.I., Mileva, K.N., Bowtell, J.L., Crichton, N., Dimitrova, N.A.: Muscle fatigue during dynamic contractions assessed by new spectral indices. Med. Sci. Sport. Exerc. 38, 1971–1979 (2006)

    Article  MATH  Google Scholar 

  13. Rogers, D.R., MacIsaac, D.T.: EMG-based muscle fatigue assessment during dynamic contractions using principal component analysis. J. Electromyogr. Kinesiol. 21, 811–818 (2011)

    Article  Google Scholar 

  14. Pereira, G.R., de Oliveira, L.F., Nadal, J.: Isometric fatigue patterns in time and time-frequency domains of triceps surae muscle in different knee positions. J. Electromyogr. Kinesiol. 21, 572–578 (2011)

    Article  Google Scholar 

  15. Huang, H., Xie, H.-B., Guo, J.-Y., Chen, H.-J.: Ant colony optimization-based feature selection method for surface electromyography signals classification. Comput. Biol. Med. 42, 30–38 (2012)

    Article  MATH  Google Scholar 

  16. Subasi, A.: Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43, 576–586 (2013)

    Article  MATH  Google Scholar 

  17. Mallor, F., Leon, T., Gaston, M., Izquierdo, M.: Changes in power curve shapes as an indicator of fatigue during dynamic contractions. J. Biomech. 43, 1627–1631 (2010)

    Article  Google Scholar 

  18. Al-Mulla, M.R., Sepulveda, F., Colley, M., Kattan, A.: Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction. In: Conference on Proceedings of IEEE Engineering in Medicine and Biology Society EMBC, pp. 2633–2638 (2009)

    Google Scholar 

  19. Mirna, A., Rafic, Y., Mohamad, K., Herman, A.: Classification of the car seats by detecting the muscular fatigue in the EMG signal. Int. J. Comput. Cogn. 3, 48–54 (2005)

    Google Scholar 

  20. Mirjalili, S., Mirjalili, S.M., Yang, X.-S.: Binary bat algorithm. Neural Comput. Appl. 25, 663–681 (2005)

    Article  Google Scholar 

  21. Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 89, 068102 (2002)

    Article  Google Scholar 

  22. Loo, C.K., Samraj, A., Lee, G.C.: Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface. Discret. Dyn. Nat. Soc. 2011, 1–8 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  23. Szabó, Z.: Information theoretical estimators toolbox. J. Mach. Learn. Res. 15, 283–287 (2014)

    MATH  Google Scholar 

  24. Nakamura, R.Y.M., Pereira, L.A.M., Costa, K.A., Rodrigues, D., Papa, J.P., Yang, X.-S.: BBA: A Binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 291–297. IEEE (2012)

    Google Scholar 

  25. Shang, C., Barnes, D.: Combining support vector machines and information gain ranking for classification of mars McMurdo panorama images. In: 2010 IEEE International Conference on Image Processing, pp. 1061–1064. IEEE (2010)

    Google Scholar 

  26. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)

    MATH  Google Scholar 

  27. Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Non Invasive Imaging and Diagnostics Lab, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India

    Navaneethakrishna Makaram & Ramakrishnan Swaminathan

Authors
  1. Navaneethakrishna Makaram
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  2. Ramakrishnan Swaminathan
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Correspondence to Navaneethakrishna Makaram .

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Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Makaram, N., Swaminathan, R. (2015). A Binary Bat Approach for Identification of Fatigue Condition from sEMG Signals. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_42

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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