一种运动迟缓的自动、客观、连续评分方法

O. M. Manzanera, E. Roosma, M. Beudel, R. Borgemeester, T. Laar, N. Maurits
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引用次数: 7

摘要

运动迟缓的评估是帕金森病诊断的关键因素。它通常使用运动障碍协会赞助的统一帕金森病评定量表(MDS-UPDRS)的修订版进行。然而,尽管它很重要,但该量表中与运动迟缓相关的项目显示出非常低的评分一致性。因此,本研究提出了一种对MDS-UPDRS中运动迟缓相关的三个项目进行自动、客观、连续评分的方法。四名临床医生对25名被诊断患有帕金森病的患者的这些项目进行评分,评分范围在0-4分之间。方向传感器用于记录每个项目执行过程中的运动。从记录的数据中得出一组特征来代表运动特征,评估者根据MDS-UPDRS对运动迟缓进行评分。这些特征和评估者的平均分数被用来创建一个模型,对每个项目的分数使用反向线性回归。估计的泛化误差表明,连续客观量表与评价者的平均分数相比,可以获得平均误差为0.50的自动评分。
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A method for automatic, objective and continuous scoring of bradykinesia
The assessment of bradykinesia is a key element in the diagnosis of Parkinson's disease. It is typically performed using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, despite its importance, the bradykinesia-related items of this scale show very low inter-rater agreement. Therefore, in this study a method for automatic, objective and continuous scoring of three of the bradykinesia-related items of the MDS-UPDRS is proposed. Four clinicians scored these items for 25 patients diagnosed with Parkinson's disease, within a range of 0-4. Orientation sensors were used to record movement during performance of each item. From the recorded data a set of features was derived to represent the movement characteristics that evaluators assess for scoring bradykinesia according to the MDS-UPDRS. These features and the averaged scores of the evaluators were used to create a model for the score on each item using backward linear regression. The estimated generalization errors indicate that the continuous objective scale can obtain an automatic score with an average error of 0.50 compared to the evaluators' averaged scores.
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