Relating Clinical and Neurophysiological Assessment of Spasticity by Machine Learning

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 relation between 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 and objective sEMG assessment may be proven useful for the assessment of interventions and follow-up of SCI patients. 1 Introduction Assessment of spasticity following spinal cord injury (SCI) often represents a significant problem. Based on classical definition of spasticity, a qualitative..