Instrumented timed up and go test and machine learning-based levodopa response evaluation: a pilot study

IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Journal of NeuroEngineering and Rehabilitation Pub Date : 2024-09-18 DOI:10.1186/s12984-024-01452-4
Jing He, Lingyu Wu, Wei Du, Fei Zhang, Shinuan Lin, Yun Ling, Kang Ren, Zhonglue Chen, Haibo Chen, Wen Su
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Abstract

The acute levodopa challenge test (ALCT) is a universal method for evaluating levodopa response (LR). Assessment of Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III) is a key step in ALCT, which is some extent subjective and inconvenience. This study developed a machine learning method based on instrumented Timed Up and Go (iTUG) test to evaluate the patients’ response to levodopa and compared it with classic ALCT. Forty-two patients with parkinsonism were recruited and administered with levodopa. MDS-UPDRS III and the iTUG were conducted in both OFF-and ON-medication state. Kinematic parameters, signal time and frequency domain features were extracted from sensor data. Two XGBoost models, levodopa response regression (LRR) model and motor symptom evaluation (MSE) model, were trained to predict the levodopa response (LR) of the patients using leave-one-subject-out cross-validation. The LR predicted by the LRR model agreed with that calculated by the classic ALCT (ICC = 0.95). When the LRR model was used to detect patients with a positive LR, the positive predictive value was 0.94. Machine learning based on wearable sensor data and the iTUG test may be effective and comprehensive for evaluating LR and predicting the benefit of dopaminergic therapy.
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仪器计时起立行走测试和基于机器学习的左旋多巴反应评估:一项试点研究
急性左旋多巴挑战试验(ALCT)是评估左旋多巴反应(LR)的通用方法。运动障碍协会帕金森病统一评定量表第三部分(MDS-UPDRS III)的评估是 ALCT 的关键步骤,但这在一定程度上存在主观性和不便性。本研究开发了一种基于仪器计时起立行走(iTUG)测试的机器学习方法来评估患者对左旋多巴的反应,并将其与经典的ALCT进行了比较。研究人员招募了42名帕金森病患者,并为其服用左旋多巴。MDS-UPDRS III 和 iTUG 分别在停药和用药状态下进行。从传感器数据中提取了运动参数、信号时域和频域特征。采用 "留一弃一 "交叉验证方法训练了两个 XGBoost 模型,即左旋多巴反应回归(LRR)模型和运动症状评估(MSE)模型,以预测患者的左旋多巴反应(LR)。LRR 模型预测的左旋多巴反应与经典 ALCT 计算的左旋多巴反应一致(ICC = 0.95)。当 LRR 模型用于检测 LR 为阳性的患者时,其阳性预测值为 0.94。基于可穿戴传感器数据和 iTUG 测试的机器学习可有效、全面地评估 LRR 并预测多巴胺能治疗的益处。
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来源期刊
Journal of NeuroEngineering and Rehabilitation
Journal of NeuroEngineering and Rehabilitation 工程技术-工程:生物医学
CiteScore
9.60
自引率
3.90%
发文量
122
审稿时长
24 months
期刊介绍: Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.
期刊最新文献
Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking. Immersive virtual reality for learning exoskeleton-like virtual walking: a feasibility study. Instrumented assessment of lower and upper motor neuron signs in amyotrophic lateral sclerosis using robotic manipulation: an explorative study. Rest the brain to learn new gait patterns after stroke. Effects of virtual reality rehabilitation after spinal cord injury: a systematic review and meta-analysis.
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