Relating clinical and neurophysiological assessment of spasticity by machine learning

  • Zupan B
  • Stokić D
  • Bohanec M
 et al. 
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Abstract

Spasticity following spinal cord injury (SCI) is most often assessed clinically using a five-point Ashworth score (AS). A more objective assessment of altered motor control may be achieved by using a comprehensive protocol based on a surface electromyographic (sEMG) activity recorded from thigh and leg muscles. However, the relationship between the clinical and neurophysiological assessments is still unknown. In this paper we employ three different classification methods to investigate this relationship. The experimental results indicate that, if the appropriate set of sEMG features is used, the neurophysiological assessment is related to clinical findings and can be used to predict the AS. A comprehensive sEMG assessment may be proven useful as an objective method of evaluating the effectiveness of various interventions and for follow-up of SCI patients.

Author-supplied keywords

  • Ashworth score
  • Classification
  • Clinical assessment of spasticity
  • Discriminant analysis
  • Machine learning
  • Neurophysiological assessment of spasticity
  • Spasticity assessment

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Authors

  • Blaz ZupanFaculty of Computer and Information Science, University of Ljubljana

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  • Dobrivoje S. Stokić

  • Marko Bohanec

  • Michael M. Priebe

  • Arthur M. Sherwood

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