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
{"title":"Instrumented timed up and go test and machine learning-based levodopa response evaluation: a pilot study","authors":"Jing He, Lingyu Wu, Wei Du, Fei Zhang, Shinuan Lin, Yun Ling, Kang Ren, Zhonglue Chen, Haibo Chen, Wen Su","doi":"10.1186/s12984-024-01452-4","DOIUrl":null,"url":null,"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.","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"194 1","pages":""},"PeriodicalIF":5.2000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of NeuroEngineering and Rehabilitation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12984-024-01452-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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
Telerehabilitation using a 2-D planar arm rehabilitation robot for hemiparetic stroke: a feasibility study of clinic-to-home exergaming therapy. Therapeutic effects of powered exoskeletal robot-assisted gait training in inpatients in the early stage after stroke: a pilot case-controlled study. Non-invasive brain stimulation enhances motor and cognitive performances during dual tasks in patients with Parkinson's disease: a systematic review and meta-analysis. Myoelectric motor execution and sensory training to treat chronic pain and paralysis in a replanted arm: a case study. Selective nociceptive modulation using a novel prototype of transcutaneous kilohertz high-frequency alternating current stimulation: a crossover double-blind randomized sham-controlled trial.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1