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

نویسندگان

  • Hua Cao
  • Olivier Agnani
  • Laurent Peyrodie
  • Samuel Boudet
چکیده

ing is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For other copying, reprint or republication permission, write to IEEE Copyrights Manager, IEEE Service Center, 445 Hoes Lane, Piscataway, NJ 08854. All rights reserved. IEEE Catalog Number (Electronic): CFP1229C-ART ISBN (Electronic): 978-1-61284-101-4 ISSN: 2151-7622 Printed copies of this publication are available from: Curran Associates, Inc 57 Morehouse Lane Red Hook, NY 12571 USA Phone: (845) 758-0400 Fax: (845) 758-2633 E-mail: [email protected] Produced by IEEE eXpress Conference Publishing For information on producing a conference proceedings and receiving an estimate, contact [email protected] http://www.ieee.org/conferencepublishing ha l-0 08 38 41 8, v er si on 1 25 J un 2 01 3 Automatic Assessment of Expanded Disability Status Scale (EDSS) in Multiple Sclerosis Using a Decision Tree Hua Cao a,b,c* , Olivier Agnani a,b,d,e , Laurent Peyrodie a,b,c,f , Samuel Boudet a,b,d , Cécile Donzé a,b,d,e a Univ Nord de France F-59000 Lille, France b UCLille F-59000 Lille, France c Unité de Traitement des Signaux Biomédicaux Hautes Etudes d’Ingénieur Lille, France * [email protected] d Faculté Libre de Médecine Groupe Hospitalier de l’Institut Catholique Lillois F-59000 Lille, France e Service de médecine physique et de rééducation fonctionnelle Hôpital de Saint-Philibert Lomme, France f Laboratoire d’Automatique et Génie Informatique et Signal Université de Lille 1 Lille, France 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 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.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. Keywords-multiple sclerosis (MS); expanded disability status scale (EDSS); center of pressure (COP); recurrence quantification analysis (RQA); decision tree

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تاریخ انتشار 2013