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 the clinical and neurophysiological assessments is still unknown. In this paper we employ three di#erent 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 way of evaluating the e#ectiveness of various interventions and for follow-up of SCI patients. Keywords spasticity assessment clinical assessment of spasticity neurophysiological assessment of spasticity Ashworth Score classificat..