Computer-aided analysis of gait rhythm fluctuations in amyotrophic lateral sclerosis

Original Article


Deterioration of motor neurons due to amyotrophic lateral sclerosis (ALS) would affect the strides from one gait cycle to the next. Computer-assisted techniques are useful for gait analysis, and also have high potential in quantitatively monitoring the pathological progression. In this paper, we applied the signal turns count method to measure the fluctuations in the swing-interval time series recorded from 16 healthy control subjects and 13 patients with ALS. The swing-interval turns count (SWITC) parameter derived with the threshold of 0.06 s presented a significant difference (p < 0.001) between the healthy control subjects and ALS patients. Besides the SWITC, we also computed the averaged stride interval (ASI), which is usually longer in the patient with ALS (p < 0.0001), to characterize the gait patterns of ALS patients. In the pattern classification experiments, the Fisher’s linear discriminant analysis (FLDA) and the least squares support vector machine (LS-SVM), both input with the SWITC and ASI features, were evaluated using the leave-one-out cross-validation method. The results showed that the LS-SVM with sigmoid kernels was able to provide a classification accurate rate of 89.66% and an area of 0.9629 under the receiver operating characteristic (ROC) curve, which were superior to those obtained with the linear classifier in the form of FLDA.


Gait analysis Movement disorders Amyotrophic lateral sclerosis Pattern classification Support vector machine Rehabilitation engineering 


  1. 1.
    Akay M, Sekine M, Tamura T, Higashi Y, Fujimoto T (2003) Unconstrained monitoring of body motion during walking. IEEE Eng Med Biol Mag 22(3):104–109CrossRefGoogle Scholar
  2. 2.
    Akay M, Sekine M, Tamura T, Higashi Y, Fujimoto T (2004) Fractal dynamics of body motion in post-stroke hemiplegic patients during walking. J Neural Eng 1(2):111–116CrossRefGoogle Scholar
  3. 3.
    Brooks BR (1996) Natural history of ALS: symptoms, strength, pulmonary function, and disability. Neurology 47(4 Suppl 2):S71–S81Google Scholar
  4. 4.
    Brown RH (1997) Amyotrophic lateral sclerosis: insights from genetics. Arch Neurol 54(10):1246–1250Google Scholar
  5. 5.
    Brown RH, Meininger V, Swash M (1999) Amyotrophic lateral sclerosis. Martin Dunitz Publishers, London, UKGoogle Scholar
  6. 6.
    Cortes C, Vapnik VN (1995) Support-vector networks. Mach Learn 20(3):273–297MATHGoogle Scholar
  7. 7.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York, NYMATHGoogle Scholar
  8. 8.
    Emborg J, Spaich EG, Andersen OK (2009) Withdrawal reflexes examined during human gait by ground reaction forces: site and gait phase dependency. Med Biol Eng Comput 47(1):29–39. doi:10.1007/s11517-008-0396-x CrossRefGoogle Scholar
  9. 9.
    Goldfarb BJ, Simon SR (1984) Gait patterns in patients with amyotrophic lateral sclerosis. Arch Phys Med Rehab 65(2):61–65Google Scholar
  10. 10.
    Hahn GJ, Shapiro SS (1994) Statistical models in engineering. Wiley, Hoboken, NJMATHGoogle Scholar
  11. 11.
    Hausdorff JM, Alexander NB (2005) Gait disorders: evaluation and management. Informa Healthcare, New York, NYGoogle Scholar
  12. 12.
    Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AL, Kaliton D, Goldberger AL (2000) Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J Appl Physiol 88(6):2045–2053Google Scholar
  13. 13.
    Hausdorff JM, Mitchell SL, Firtion R, Peng CK, Cudkowicz ME, Wei JY, Goldberger AL (1997) Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J Appl Physiol 82(1):262–269Google Scholar
  14. 14.
    Hausdorff JM, Peng CK, Ladin Z, Wei JY, Goldberger AL (1995) Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. J Appl Physiol 78(1):349–358Google Scholar
  15. 15.
    Hausdorff JM, Purdon PL, Peng CK, Ladin Z, Wei JY, Goldberger AL (1996) Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. J Appl Physiol 80(5):1448–1457Google Scholar
  16. 16.
    Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Englewood Cliffs, NJGoogle Scholar
  17. 17.
    Hirano A (1996) Neuropathology of ALS: an overview. Neurology 47(4 Suppl 2):63–66Google Scholar
  18. 18.
    Lau HY, Tong KY, Zhu HL (2008) Support vector machine for classification of walking conditions using miniature kinematic sensors. Med Biol Eng Comput 46(6):563–573. doi: 10.1007/s11517-008-0327-x CrossRefGoogle Scholar
  19. 19.
    Leigh PN, Meldrum BS (1996) Excitotoxicity in ALS. Neurology 47(6 Suppl 4):221–227Google Scholar
  20. 20.
    Metz C, Herman B, Shen J (1998) Maximum-likelihood estimation of ROC curves from continuously-distributed data. Stat Med 17(9):1033–1053CrossRefGoogle Scholar
  21. 21.
    Meyer AR, Wang M, Smith PA, Harris GF (2007) Modeling initial contact dynamics during ambulation with dynamic simulation. Med Biol Eng Comput 45(4):387–394. doi:10.1007/s11517-007-0166-1 CrossRefGoogle Scholar
  22. 22.
    Moody GB, Mark RG, Goldberger AL (2001) PhysioNet: a web-based resource for the study of physiologic signals. IEEE Eng Med Biol Mag 20(3):70–75CrossRefGoogle Scholar
  23. 23.
    Nash SG, Sofer A (1995) Linear and nonlinear programming. McGraw-Hill, Columbus, OHGoogle Scholar
  24. 24.
    Rangayyan RM (2002) Biomedical signal analysis: a case-study approach. IEEE and Wiley, New York, NYGoogle Scholar
  25. 25.
    Rangayyan RM, Wu YF (2009) Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Ann Biomed Eng 37(1):156–163. doi:10.1007/s10439-008-9601-1 CrossRefGoogle Scholar
  26. 26.
    Ropper AH, Brown RH (2005) Adams and Victor’s principles of neurology, 8th edn. McGraw-Hill, New York, NYGoogle Scholar
  27. 27.
    Sekine M, Akay M, Tamura T, Higashi Y (2004) Fractal dynamics of body motion in patients with Parkinson’s disease. J Neural Eng 1(1):8–15CrossRefGoogle Scholar
  28. 28.
    Sekine M, Tamura T, Akay M, Fujimoto T, Togawa T, Fukui Y (2002) Discrimination of walking patterns using wavelet-based fractal analysis. IEEE Trans Neural Syst Rehab Eng 10(3):188–196. doi:10.1109/TNSRE.2002.802879 CrossRefGoogle Scholar
  29. 29.
    Sharma KR, Kent-Braun JA, Majumdar S, Huang Y, Mynhier M, Weiner MW, Miller RG (1995) Physiology of fatigue in amyotrophic lateral sclerosis. Neurology 45(4):733–740Google Scholar
  30. 30.
    Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific Publishing, SingaporeMATHGoogle Scholar
  31. 31.
    Vapnik VN (1998) Statistical learning theory. Wiley, New York, NYMATHGoogle Scholar
  32. 32.
    Willison RG (1964) Analysis of electrical activity in healthy and dystrophic muscle in man. J Neurol Neurosurg Psychiatry 27(5):386–394CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2009

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringRyerson UniversityTorontoCanada

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