TITLE:
Automatic Assessment of Expanded Disability Status Scale (EDSS) in Multiple Sclerosis Using a Decision Tree
AUTHORS:
Hua Cao, Olivier Agnani, Laurent Peyrodie, Cécile Donzé
KEYWORDS:
Multiple Sclerosis (MS); Expanded Disability Status Scale (EDSS); Center of Pressure (COP); Recurrence Quantification Analysis (RQA); Decision Tree
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
December
20,
2013
ABSTRACT: The expanded disability status scale (EDSS) is frequently
used to classify the patients with multiple sclerosis (MS). We presented in this
paper a novel method to automatically assess the EDSS score from posturologic data
(center of pres-sure signals) using a decision tree. Two groups of participants (one
for learning and the other for test) with EDSS rang-ing from 0 to 4.5 performed
our balance experiment with eyes closed. Two linear measures (the length and the
surface) and twelve non-linear measures (the recurrence rate, the Shannon entropy,
the averaged diagonal line length and the trapping time for the position, the instantaneous
velocity and the instantaneous acceleration of the center of pressure respectively)
were calculated for all the participants. Several decision trees were constructed
with learning data and tested with test data. By comparing clinical and estimated
EDSS scores in the test group, we selected one decision tree with five measures
which revealed a 75% of agreement. The results have signified that our tree model
is able to auto-matically assess the EDSS scores and that it is possible to distinguish
the EDSS scores by using linear and non-linear postural sway measures.