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

作　　者：Hua Cao Olivier Agnani Laurent Peyrodie Samuel Boudet Cecile Donze 

机构地区：[1]UniversiteLille Nord de France,Lille,France [2]UCLille,Lille,France [3]Unitede Traitement des Signaux Biomedicaux,Hautes Etudes d’Ingenieur,Lille,France [4]Faculte Libre de Medecine,Groupe Hospitalier de l’Institut Catholique Lillois,Lille,France [5]Service de Medecine Physique et de Reeducation Fonctionnelle,Hopital de Saint-Philibert,Lomme,France [6]Laboratoire d’Automatique et Genie Informatique et Signal,Universitede Lille 1,Lille,France

出　　处：《Engineering（科研）》2013年第10期566-569,共4页工程（英文）（1947-3931）

摘　　要：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.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.

关 键 词：Multiple Sclerosis (MS) Expanded Disability Status Scale (EDSS) Center of Pressure (COP) Recurrence Quantification Analysis (RQA) Decision Tree 

分 类 号：R74[医药卫生—神经病学与精神病学]