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一种使用方向传感器和分类算法对运动迟缓进行自动客观评分的方法。

A Method for Automatic and Objective Scoring of Bradykinesia Using Orientation Sensors and Classification Algorithms.

作者信息

Martinez-Manzanera O, Roosma E, Beudel M, Borgemeester R W K, van Laar T, Maurits N M

出版信息

IEEE Trans Biomed Eng. 2016 May;63(5):1016-1024. doi: 10.1109/TBME.2015.2480242. Epub 2015 Sep 18.

Abstract

Correct assessment of bradykinesia is a key element in the diagnosis and monitoring of Parkinson's disease. Its evaluation is based on a careful assessment of symptoms and it is quantified using rating scales, where the Movement Disorders Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard. Regardless of their importance, the bradykinesia-related items show low agreement between different evaluators. In this study, we design an applicable tool that provides an objective quantification of bradykinesia and that evaluates all characteristics described in the MDS-UPDRS. Twenty-five patients with Parkinson's disease performed three of the five bradykinesia-related items of the MDS-UPDRS. Their movements were assessed by four evaluators and were recorded with a nine degrees-of-freedom sensor. Sensor fusion was employed to obtain a 3-D representation of movements. Based on the resulting signals, a set of features related to the characteristics described in the MDS-UPDRS was defined. Feature selection methods were employed to determine the most important features to quantify bradykinesia. The features selected were used to train support vector machine classifiers to obtain an automatic score of the movements of each patient. The best results were obtained when seven features were included in the classifiers. The classification errors for finger tapping, diadochokinesis and toe tapping were 15-16.5%, 9.3-9.8%, and 18.2-20.2% smaller than the average interrater scoring error, respectively. The introduction of objective scoring in the assessment of bradykinesia might eliminate inconsistencies within evaluators and interrater assessment disagreements and might improve the monitoring of movement disorders.

摘要

正确评估运动迟缓是帕金森病诊断和监测的关键要素。其评估基于对症状的仔细评估,并使用评分量表进行量化,其中运动障碍协会赞助的统一帕金森病评分量表修订版(MDS-UPDRS)是金标准。尽管其很重要,但与运动迟缓相关的项目在不同评估者之间的一致性较低。在本研究中,我们设计了一种适用工具,该工具可对运动迟缓进行客观量化,并能评估MDS-UPDRS中描述的所有特征。25名帕金森病患者完成了MDS-UPDRS中五个与运动迟缓相关项目中的三个。他们的动作由四名评估者进行评估,并用一个九自由度传感器进行记录。采用传感器融合技术获得动作的三维表示。基于所得信号,定义了一组与MDS-UPDRS中描述的特征相关的特征。采用特征选择方法确定量化运动迟缓的最重要特征。所选特征用于训练支持向量机分类器,以获得每位患者动作的自动评分。当分类器中包含七个特征时,获得了最佳结果。手指敲击、轮替动作和足趾敲击的分类误差分别比评估者间平均评分误差小15 - 16.5%、9.3 - 9.8%和18.2 - 20.2%。在运动迟缓评估中引入客观评分可能会消除评估者内部的不一致性和评估者间的评估分歧,并可能改善对运动障碍的监测。

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