References

• 1. 1)
     • 16. Merlo, A., Farina, D.: ‘A fast and reliable technique for muscle activity detection from surface EMG signals’, IEEE Trans. Biomed. Eng., 2003, 50, (3), pp. 316–323 (doi: 10.1109/TBME.2003.808829).
  2. 2)
     • 4. Cristini, J.: ‘Misdiagnosis and missed diagnoses in patients with ALS’, J. Am. Acad. Physician Assistants, 2006, 19, (7), pp. 29–35 (doi: 10.1097/01720610-200607000-00006).
  3. 3)
     • 24. Flandrin, P., Rilling, G., Goncalves, P.: ‘Empirical mode decomposition as a filter bank’, IEEE Signal Process. Lett., 2004, 11, (2), pp. 112–114 (doi: 10.1109/LSP.2003.821662).
  4. 4)
     • 1. Yousefi, J., Hamilton-Wright, A.: ‘Characterizing EMG data using machine-learning tools’, Comput. Biol. Med., 2014, 51, pp. 1–13 (doi: 10.1016/j.compbiomed.2014.04.018).
  5. 5)
     • 16. Bajaj, V., Pachori, R.B.: ‘Classification of seizure and non-seizure EEG signals using empirical mode decomposition’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (6), pp. 1135–1142 (doi: 10.1109/TITB.2011.2181403).
  6. 6)
     • 5. Ko, K.D., El-Ghazawi, T., Kim, D., et al: ‘Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach’. IEEE Conf. Computational Intelligence in Bioinformatics and Computational Biology, May 2014, pp. 1–6.
  7. 7)
     • 14. Pattichis, C.S., Elia, A.G.: ‘Autoregressive cepstral analyses of motor unit action potentials’, Med. Eng. Phys., 1999, 21, (6-7), pp. 405–419 (doi: 10.1016/S1350-4533(99)00072-7).
  8. 8)
     • 8. Kasi, P.K., Krivickas, L.S., Melvin, M., et al: ‘Characterization of motor unit behavior in patients with amyotrophic lateral sclerosis’. Fourth Int. IEEE/EMBS Conf. on Neural Engineering, April 2009, pp. 10–13.
  9. 9)
     • 34. Vapnik, V.: ‘The nature of statistical learning theory’ (Springer-Verlag, New York, 1995).
  10. 10)
      • 17. Suykens, J.A.K., Vandewalle, J.: ‘Least squares support vector machine classifiers’, Neural Process. Lett., 1999, 9, (3), pp. 293–300 (doi: 10.1023/A:1018628609742).
  11. 11)
      • 31. Ohsawa, Y., Tsumoto, S.: ‘Chance discoveries in real world decision making: data- based interaction of human intelligence and artificial intelligence’ (Springer Science & Business Media, 2006), pp. 109–110.
  12. 12)
      • 17. Huang, N., Shen, Z., Long, S., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’, Proc. R. Soc. Lond. A, 1971, 454, pp. 903–995 (doi: 10.1098/rspa.1998.0193).
  13. 13)
      • 11. Fattah, S.A., Doulah, A.B.M.S.U., Iqbal, M.A., et al: ‘Identification of motor neuron disease using wavelet domain features extracted from EMG signal’. IEEE Int. Symp. on Circuits and Systems (ISCAS), May 2013, pp. 1308–1311.
  14. 14)
      • 6. Kasi, P.K., Krivickas, L.S., Meister, M., et al: ‘Motor unit firing characteristics in patients with amyotrophic lateral sclerosis’. Annual Northeast IEEE Conf. on Bioengineering, June 2009, pp. 1–2.
  15. 15)
      • 3. Rosenfeld, J., Swash, M.: ‘Lumping or splitting ALS, PLS, PMA, and the other motor neuron diseases’, Neurology, 2006, 66, (5), pp. 624–625 (doi: 10.1212/01.wnl.0000205597.62054.db).
  16. 16)
      • 2. Tsai, A.C., Luh, J.J., Lin, T.T.: ‘A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition’, Expert Syst. Appl., 2015, 42, (7), pp. 3327–3341 (doi: 10.1016/j.eswa.2014.11.044).
  17. 17)
      • 19. Maitrot, A., Lucas, M.F., Doncarli, C., et al: ‘Signal-dependent wavelets for electromyogram classification’, Med. Biol. Eng. Comput., 2005, 43, (4), p. 487 (doi: 10.1007/BF02344730).
  18. 18)
      • 23. Fusfeld, R.: ‘Classification of the electromyogram by a pattern-recognition method’, Med. Biol. Eng. Comput., 1982, 20, pp. 496–500 (doi: 10.1007/BF02442412).
  19. 19)
      • 24. Nikolic, M.: ‘Detailed analysis of clinical electromyography signals EMG decomposition, findings and firing pattern analysis in controls and patients with myopathy and amytrophic lateral sclerosis’. PhD Thesis, Faculty of Health Science, University of Copenhagen, August 2001.
  20. 20)
      • 15. Subasi, A.: ‘Classification of EMG signals using combined features and soft computing techniques’, Appl. Soft Comput., 2012, 12, (8), pp. 2188–2198 (doi: 10.1016/j.asoc.2012.03.035).
  21. 21)
      • 21. Gokgoz, E., Subasi, A.: ‘Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders’, J. Med. Syst., 2014, 38, (4), pp. 1–10 (doi: 10.1007/s10916-014-0031-3).
  22. 22)
      • 9. Pal, P., Mohanty, N., Kushwaha, A., et al: ‘Feature extraction for evaluation of Muscular Atrophy’. IEEE Int. Conf. on Computational Intelligence and Computing Research, December 2010, pp. 1–4.
  23. 23)
      • 33. Zhou, S.M., Gan, J.Q., Sepulveda, F.: ‘Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface’, Inf. Sci., 2008, 178, (6), pp. 1629–1640 (doi: 10.1016/j.ins.2007.11.012).
  24. 24)
      • 30. Pachori, R.B., Hewson, D., Snoussi, H., et al: ‘Postural time-series analysis using empirical mode decomposition and second-order difference plots’. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, April 2009, pp. 537–540.
  25. 25)
      • 12. Fattah, S.A., Iqbal, Md.A., Jumana, M.A., et al: ‘Identifying the motor neuron disease in EMG signal using time and frequency domain features with comparison’, Int. J. Signal Image Process., 2012, 3, (2), pp. 99–114 (doi: 10.5121/sipij.2012.3207).
  26. 26)
      • 29. Cohen, M.E., Hudson, D.L., Deedwania, P.: ‘Applying continuous chaotic modeling to cardiac signal analysis’, IEEE Eng. Med. Biol. Mag., 1996, 15, pp. 97–102 (doi: 10.1109/51.537065).
  27. 27)
      • 7. Fattah, S.A., Doulah, A.B.M.S.U., Jumana, M.A., et al: ‘Evaluation of different time and frequency domain features of motor neuron and musculoskeletal diseases’, Int. J. Comput. Appl., 2012, 43, (23), pp. 34–40.
  28. 28)
      • 13. Doulah, A.B.M.S., Jumana, M.A.: ‘ALS disease detection in EMG using time-frequency method’. Int. Conf. on Informatics, Electronics & Vision (ICIEV), May 2012, pp. 648–651.
  29. 29)
      • 22. Wang, G., Ren, O.: ‘Classification of surface electromyographic signals by means of multifractal singularity spectrum’, Med. Biol. Eng. Comput., 2013, 51, pp. 277–284 (doi: 10.1007/s11517-012-0990-9).
  30. 30)
      • 17. Katsis, C.D., Exarchos, T.P., Papaloukas, C., et al: ‘A two-stage method for MUAP classification based on EMG decomposition’, Comput. Biol. Med., 2007, 37, (9), pp. 1232–1240 (doi: 10.1016/j.compbiomed.2006.11.010).
  31. 31)
      • 20. Istenic, R., Kaplanis, P.A., Pattichis, C.S., et al: ‘Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders’, Med. Biol. Eng. Comput., 2010, 48, (8), pp. 773–781 (doi: 10.1007/s11517-010-0629-7).
  32. 32)
      • 10. Doulah, A.B.M.S.U., Fattah, S.A.: ‘Neuromuscular disease classification based on mel frequency cepstrum of motor unit action potential’. Int. Conf. on Electrical Engineering and Information & Communication Technology (ICEEICT), April 2014, pp. 1–4.
  33. 33)
      • 32. Cohen, L., Lee, C.: ‘Instantaneous bandwidth for signals and spectrogram’, 1990, pp. 2450–2454.
  34. 34)
      • 18. Wang, G., Wang, Z., Chen, W., et al: ‘Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion’, Med. Biol. Eng. Comput., 2006, 44, (10), pp. 865–872 (doi: 10.1007/s11517-006-0100-y).
  35. 35)
      • 13. Pachori, R.B., Bajaj, V.: ‘Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition’, Comput. Methods Programs Biomed., 2011, 104, (3), pp. 373–381 (doi: 10.1016/j.cmpb.2011.03.009).