Automatic Assessment of Expanded Disability Status Scale (EDSS) in Multiple Sclerosis Using a Decision Tree - Archive ouverte HAL
Communication Dans Un Congrès Année : 2012

Automatic Assessment of Expanded Disability Status Scale (EDSS) in Multiple Sclerosis Using a Decision Tree

Résumé

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 pressure signals) using a decision tree. Two groups of participants (one for learning and the other for test) with EDSS ranging 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 automatically assess the EDSS scores and that it is possible to distinguish the EDSS scores by using linear and nonlinear postural sway measures.
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Dates et versions

hal-00838418 , version 1 (25-06-2013)

Identifiants

  • HAL Id : hal-00838418 , version 1

Citer

Hua Cao, Laurent Peyrodie, Samuel Boudet, Cécile Donzé. Automatic Assessment of Expanded Disability Status Scale (EDSS) in Multiple Sclerosis Using a Decision Tree. The 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2012), May 2012, Shangai, China. pp.759-762. ⟨hal-00838418⟩
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