Prediction of the hand function part of the Fugl-Meyer scale after stroke using an automatic quantitative assessment system

Brain-X Pub Date : 2023-08-22 DOI:10.1002/brx2.26
Shugeng Chen, Xiaolei Lin, Jianghong Fu, Yeye Qian, Zihang Chen, Zhanbo Huang, Qiang Liu, Xiaofeng Lu, Jie Jia
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Abstract

Hand function assessment is an essential component of the process of stroke rehabilitation because of the high incidence of hand motor dysfunction. In terms of the manual evaluation of hand function, the Fugl-Meyer scale is a recommended scale with high reliability and validity. However, the need for accurate assessments and increasing developments in technology has led to the promotion of automatic quantitative assessment systems for the hand. In this study, we collected quantitative data on hand function with an automatic system and the upper limb Fugl-Meyer assessment (FMA) from 79 people with stroke. We developed decision tree (DT) and gradient-boosted decision tree (GBDT) predictive models for the Fugl-Meyer score using features extracted from the Hand Automatic Quantitative Assessment System (HAQAS). Predictive performances were compared between these models regarding the predictive accuracy and Cohen's kappa. There were high correlations between features automatically collected by the HAQAS and the Fugl-Meyer scale in all the sub-items, with the maximal correlations all being over 0.5, indicating the high validity of the HAQAS in automatic FMA prediction. Hand functions were more highly correlated (average correlation coefficient 0.90) with HAQAS features than wrist functions (average correlation coefficient 0.54), and the GBDT achieved higher predictive accuracies and agreement than the DT algorithm. We conclude that the HAQAS is feasible for stroke patients with hand dysfunction and convenient for clinicians and therapists. This study was registered in the Chinese Clinical Trial Registry (ChiCTR1800019098).

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用自动定量评估系统预测中风后Fugl-Meyer量表的手功能部分
手功能评估是中风康复过程中的一个重要组成部分,因为手部运动功能障碍的发生率很高。就手动评估手功能而言,Fugl-Meyer量表是一种推荐的量表,具有较高的信度和有效性。然而,对准确评估的需求和技术的不断发展导致了手部自动定量评估系统的推广。在这项研究中,我们使用自动系统收集了79名中风患者的手功能和上肢Fugl-Meyer评估(FMA)的定量数据。我们使用从手动定量评估系统(HAQAS)中提取的特征,为Fugl-Meyer评分开发了决策树(DT)和梯度增强决策树(GBDT)预测模型。比较了这些模型在预测准确性和Cohenκ方面的预测性能。在所有子项中,HAQAS和Fugl-Meyer量表自动收集的特征之间存在高度相关性,最大相关性均超过0.5,表明HAQAS在FMA自动预测中具有较高的有效性。与手腕功能(平均相关系数0.54)相比,手功能与HAQAS特征的相关性更高(平均相关性系数0.90),GBDT比DT算法实现了更高的预测精度和一致性。我们的结论是,HAQAS对手功能障碍的中风患者是可行的,并且对临床医生和治疗师来说是方便的。本研究已在中国临床试验注册中心(ChiCTR1800019098)注册。
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