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 ...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.展开更多
文摘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.
文摘目的:探讨多发性硬化(Multiple sclerosis,MS)的临床和影像学表现。材料与方法:收集资料完整符合McDonald诊断标准的180例MS患者作为研究对象,按照国际MS中心制定的扫描序列进行扫描,分析MS主要临床特点、脑和脊髓病灶影像特点以及与临床残疾状态功能评分(Expanded disability status scale,EDSS)的相关性。结果:(1)本组MS患者以上呼吸道感染为主要诱因之一(27.78%),以肢体无力为最常见的症状(54.4%)。(2)按照病灶受累的部位分类:单纯脑部受累45.56%,单纯脊髓受累29.44%,脑和脊髓均受累25%,三者之间的EDSS评分有统计学差别(P<0.05)。(3)下颈髓和上胸髓最易受累,≤3个节段的病灶数占74.49%,累及1～2个节段脊髓病灶例数最多。结论:结合临床和MRI影像特征,有利于MS的诊断和监测病灶的发展。
