Hu Chengpeng, Ti Chun Hang Eden, Shi Xiangqian, Yuan Kai, Leung Thomas W H, Tong Raymond Kai-Yu
Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
Division of Neurology, Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
Arch Phys Med Rehabil. 2025 Feb;106(2):206-215. doi: 10.1016/j.apmr.2024.08.015. Epub 2024 Aug 30.
To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE.
A prospective longitudinal multicenter cohort study. We collected preintervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from 8 time windows during MVC-EMG (0.1-5s) to identify subjects' motor intention. Classification and regression tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor improvements was further investigated.
Nine rehabilitation centers.
Chronic stroke survivors (N=131), including 87 for derivation sample, and 44 for validation sample.
All participants underwent 20-session robotic hand training (40min/session, 3-5sessions/wk).
Prediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy.
The best model comprised FMAUE (cutoff score, 46) and peak activity of ED from 1-second MVC-EMG (MVC-EMG 4.604 times higher than resting EMG), which demonstrated significantly higher prediction accuracy (AUC, 0.807) than other time windows or solely using clinical scores (AUC, 0.595). In external validation, this model displayed robust prediction (AUC, 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases.
This study presents a prediction model for intention-driven robotic hand training in chronic stroke survivors. It highlights significance of capturing motor intention through 1-second EMG window as a predictor for MCID improvement in UE motor function after 20-session robotic training. Survivors in 2 conditions showed high percentage of clinical motor improvement: moderate-to-high motor intention and low-to-moderate function; as well as high intention and high function.
利用患侧上肢残余自主肌电图（EMG）信号，推导并验证意向驱动型机器人手训练后上肢（UE）运动功能最小临床重要差异（MCID）的预测模型。
一项前瞻性纵向多中心队列研究。我们收集了干预前的候选预测指标：人口统计学数据、临床特征、UE Fugl-Meyer评估（FMAUE）、动作研究臂测试得分，以及最大自主收缩（MVC）期间通过EMG测量的指屈肌和指伸肌（ED）的运动意向。对于EMG测量，考虑到中风幸存者移动瘫痪手的挑战，在MVC-EMG（0.1-5秒）期间从8个时间窗中提取峰值信号，以识别受试者的运动意向。采用分类和回归树算法预测FMAUE达到MCID的幸存者。进一步研究了预测指标与运动改善之间的关系。
九个康复中心。
慢性中风幸存者（N=131），其中87例用于推导样本，44例用于验证样本。
所有参与者均接受20节机器人手训练课程（每节40分钟，每周3-5节）。
通过受试者工作特征曲线下面积（AUC）评估模型的预测效能。最佳有效模型为最终模型，并使用AUC和总体准确率进行验证。
最佳模型包括FMAUE（截断分数为46）和1秒MVC-EMG时ED的峰值活动（MVC-EMG比静息EMG高4.604倍），其预测准确率（AUC，0.807）显著高于其他时间窗或仅使用临床评分（AUC，0.595）。在外部验证中，该模型显示出强大的预测能力（AUC，0.916）。观察到ED-EMG与FMAUE增加之间存在显著的二次关系。
本研究提出了一种针对慢性中风幸存者意向驱动型机器人手训练的预测模型。它强调了通过1秒EMG窗口捕捉运动意向作为20节机器人训练后UE运动功能MCID改善预测指标的重要性。处于两种情况下的幸存者临床运动改善比例较高：中到高运动意向和低到中度功能；以及高意向和高功能。
