B. Zupan et al., RELATING CLINICAL AND NEUROPHYSIOLOGICAL ASSESSMENT OF SPASTICITY BY MACHINE LEARNING, International journal of medical informatics, 49(2), 1998, pp. 243-251
Citations number
13
Categorie Soggetti
Computer Science Information Systems","Medical Informatics","Computer Science Information Systems
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.