Mehmet Rahmi CANAL, Veysel ALCAN, Murat ZİNNUROĞLU

Using fuzzy logic for diagnosis and classification of spasticity

Background/aim: Spasticity is generally defined as a sensory-motor control disorder. However, there is no pathophysiological mechanism or appropriate measurement and evaluation standards that can explain all aspects of a possible spasticity occurrence. The objective of this study is to develop a fuzzy logic classifier (FLC) diagnosis system, in which a quantitative evaluation is performed by surface electromyography (EMG), and investigate underlying pathophysiological mechanisms of spasticity. Materials and methods: Surface EMG signals recorded from the tibialis anterior and medial gastrocnemius muscles of hemiplegic patients with spasticity and a healthy control group were analyzed in standing, resting, dorsal flexion, and plantar flexion positions. The signals were processed with different methods: by using their amplitudes in the time domain, by applying short-time Fourier transform, and by applying wavelet transform. A Mamdani-type multiple-input, single-output FLC with 64 rules was developed to analyze EMG signals. Results: The wavelet transform provided better positive findings among all three methods used in this study. The FLC test results showed that the test was 100% sensitive to identify spasticity with 95.8% accuracy and 93.8% specificity. Conclusion: A FLC was successfully designed to detect and identify spasticity in spite of existing measurement difficulties in its nature.

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1. Pandyan AD, Gregoric M, Barnes MP, Wood D, van Wijck F, Burridge J, Hermens H, Johnson GR. Spasticity: clinical perceptions, neurological realities and meaningful measurement. Disabil Rehabil 2005; 27: 2-6.
2. Nance PW, Satkunam L, Ethans K. Spasticity management. In: Braddom RL, editor. Physical Medicine and Rehabilitation. 4th ed. Philadelphia, PA, USA: Elsevier Saunders; 2011. pp. 638-656.
3. Pandyan AD, van Wijck FMJ, Stark S, Vuadens P, Johnson GR, Barnes MP. The construct validity of a spasticity measurement device for clinical practice: An alternative to the Ashworth scales. Disabil Rehabil 2006; 28: 579-585.
4. van der Krogt HJM, Meskers CGM, de Groot JH, Klomp A, Arendzen JH. The gap between clinical gaze and systematic assessment of movement disorders after stroke. J Neuroeng Rehabil 2012; 9: 61.
5. Malhotra S, Pandyan AD, Day CR, Jones PW, Hermens HJ. Spasticity, an impairment that is poorly defined and poorly measured. Clin Rehabil 2009; 23: 651-658.
6. Pandyan AD, Johnson GR, Price CI, Curless RH, Barnes MP, Rodgers H. A review of the properties and limitations of the Ashworth and modified Ashworth scales as measures of spasticity. Clin Rehabil 1999; 13: 373-383.
7. Alibiglou L, Rymer WZ, Harvey RL, Mirbagheri MM. The relation between Ashworth scores and neuromechanical measurements of spasticity following stroke. J Neuroeng Rehabil 2008; 5: 18.
8. Lebiedowska MK, Fisk JR. Knee resistance during passive stretch in patients with hypertonia. J Neurosci Methods 2009; 179: 323- 330.
9. Calota A, Feldman A, Levin MF. Spasticity measurement based on tonic stretch reflex threshold in stroke using a portable device. Clin Neurophysiol 2008; 119: 2329-2337.
10. Starsky AJ, Sangani SG, McGuire JR, Logan B, Schmit BD. Reliability of biomechanical spasticity measurements at the elbow of people poststroke. Arch Phys Med Rehab 2005; 86: 1648-1654.
11. Katz RT, Rovai CP, Brait C, Rymer WZ. Objective quantification of spastic hypertonia: correlation with clinical findings. Arch Phys Med Rehab 1992; 73: 339-347.
12. Crone C, Johnsen LL, Biering-Sorensen F, Nielsen JB. Appearance of reciprocal facilitation of ankle extensors from ankle flexors in patients with stroke or spinal cord injury. Brain 2003; 126: 495- 507.
13. Nuyens GE, De Weerdt WJ, Spaepen AJ, Kiekens C, Feys HM. Reduction of spastic hypertonia during repeated passive knee movements in stroke patients. Arch Phys Med Rehab 2002; 83: 930-935.
14. Tekgül H, Polat M, Tosun A, Serdaroğlu G, Gökben S. Electrophysiologic assessment of spasticity in children using H-reflex. Turkish J Pediatr 2013; 55: 519-523.
15. Burridge JH, Wood DE, Hermens HJ, Voerman GE, Johnson GR, van Wijck F, Platz T, Gregoric M, Hitchcock R, Pandyan AD. Theoretical and methodological considerations in the measurement of spasticity. Disabil Rehabil 2005; 27: 69-80.
16. Levin MF. On the nature and measurement of spasticity. Clin Neurophysiol 2005; 116: 1754-1755.
17. Miner NE. An Introduction to Wavelet Theory and Analysis. Albuquerque, NM, USA: Sandia National Laboratories; 1988.
18. Kumar DK, Pah ND, Bradley A. Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE T Neur Sys Reh 2003; 11: 400-406.
19. Jobe TH, Helgason CM, Roitberg BZ. Show me the numbers: the application of numbers to medical science. Surg Neurol 2001; 56: 3-7.
20. Helgason CM, Malik DS, Cheng SC, Jobe TH, Mordeson JN. Statistical versus fuzzy measures of variable interaction in patients with stroke. Neuroepidemiology 2001; 20: 77-84.
21. Canal MR, Zinnuroğlu M, Alcan V. Development of a userfriendly patient monitoring tool for the quantitative analysis of spasticity using surface EMG. Turk J Phys Med Rehab 2013; 59: 315.
22. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kines 2000; 10: 361-374.
23. Phinyomark A, Phukpattaranont P, Limsakul C. Wavelet-based denoising algorithm for robust EMG pattern recognition. Fluct Noise Lett 2011; 10: 157-167.
24. Misiti M, Misiti Y, Oppenheim G, Poggi JM. Wavelet Toolbox: The Users Guide. Natick, MA, USA: The MathWorks Inc.; 2013.
25. LaBrunda M, LaBrunda A. Fuzzy logic in medicine. J Inf Technol Res 2008; 1: 27-33.
26. Lance JW. Pathophysiology of spasticity and clinical experience with baclofen. In: Feldman RG, Young RR, Koella WP. editors. Spasticity. Chicago, IL, USA: Year Book Medical Publishers; 1980. pp. 185-204.
27. Sheean G. Neurophysiology of spasticity. In: Barnes MP, Johnson GR. editors. Upper Motor Neurone Syndrome and Spasticity: Clinical Management and Neurophysiology. Cambridge, UK: Cambridge University Press; 2001. pp. 12-78.
28. McHugh MP, Kremenic IJ, Fox MB, Gleim GW. The role of mechanical and neural restraints to joint range of motion during passive stretch. Med Sci Sport Exerc 1998; 30: 928-932.
29. Finley JM, Perreault EJ, Dhaher YY. Stretch reflex coupling between the hip and knee: implications for impaired gait following stroke. Exp Brain Res 2008; 188: 529-540.
30. Kamper DG, Schmit BD, Rymer WZ. Effect of muscle biomechanics on the quantification of spasticity. Ann Biomed Eng 2011; 29: 1122-1134.
31. Fleuren JFM, Nederhand MJ, Hermens HJ. Influence of posture and muscle length on stretch reflex activity in poststroke patients with spasticity. Arch Phys Med Rehab 2006; 87: 981- 88.
32. Vodovnik L, Bowman BR, Bajd T. Dynamics of spastic knee joint. Med Biol Eng Comput 1984; 22: 63-69.
33. Canal MR. Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals. J Med Sys 2010; 34: 91-94.
34. Kilby J, Mawston G, Hosseini HG. Continuous wavelet analysis and classification of surface electromyography signals. In: World Congress on Medical Physics and Biomedical Engineering; 2006. pp. 1034-1036.
35. Koçer S. Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases. J Med Sys 2010; 34: 321-329.
36. Kılıç YA, Konan A, Yorgancı K, Sayek İ. A novel fuzzy-logic inference system for predicting trauma-related mortality: emphasis on the impact of response to resuscitation. Eur J Trauma Emerg S 2010; 36: 543-550.
37. Datta S, Sajja BR, He R, Wolinsky JS, Gupta RK, Narayana PA. Segmentation and quantification of black holes in multiple sclerosis. Neuroimage 2006; 29: 467-474.
38. Hilgevoord AA, Koelman JH, Bour LJ, de Visser BW. The relationship between the soleus H-reflex amplitude and vibratory inhibition in controls and spastic subjects. J Electromyogr Kinesiol 1996; 6: 253-258.