RELATING CLINICAL AND NEUROPHYSIOLOGICAL ASSESSMENT OF SPASTICITY BY MACHINE LEARNING

Spasticity following spinal cord injury (SCI) is most often assessed c linically using a five-point Ashworth score (AS). A more objective ass essment of altered motor control may be achieved by using a comprehens ive protocol based on a surface electromyographic (sEMG) activity reco rded 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 app ropriate set of sEMG features is used, the neurophysiological assessme nt is related to clinical findings and can be used to predict the AS. A comprehensive sEMG assessment may be proven useful as an objective m ethod of evaluating the effectiveness of various interventions and for follow-up of SCI patients. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.