Klinik für Neurologie, Universitätsklinikum Gießen und Marburg, Standort Marburg, Baldingerstr., 35041 Marburg, Germany.
Klinik für Neurologie, Universitätsklinikum Gießen und Marburg, Standort Marburg, Baldingerstr., 35041 Marburg, Germany.
Clin Neurophysiol. 2021 Jul;132(7):1708-1713. doi: 10.1016/j.clinph.2021.01.031. Epub 2021 Mar 13.
Parkinson's disease (PD) is a chronic neurodegenerative disorder with increasing prevalence in the elderly. Especially patients with advanced PD often require complex medication regimens due to fluctuations, that is abrupt transitions from ON to OFF or vice versa. Current gold standard to quantify PD-patients' motor symptoms is the assessment of the Unified Parkinson's Disease Rating Scale (UPDRS), which, however, is cumbersome and may depend upon investigators. This work aimed at developing a mobile, objective and unobtrusive measurement of motor symptoms in PD.
Data from 45 PD-patients was recorded using surface electromyography (sEMG) electrodes attached to a wristband. The motor paradigm consisted of a tapping task performed with and without dopaminergic medication. Our aim was to predict UPDRS scores from the sEMG characteristics with distinct regression models and machine learning techniques.
A random forest regression model outnumbered other regression models resulting in a correlation of 0.739 between true and predicted UPDRS values.
PD-patients' motor affection can be extrapolated from sEMG data during a simple tapping task. In the future, such records could help determine the need for medication changes in telemedicine applications.
Our findings support the utility of wearables to detect Parkinson's symptoms and could help in developing tailored therapies in the future.
帕金森病（PD）是一种慢性神经退行性疾病，在老年人中的发病率不断增加。特别是晚期 PD 患者由于波动，即突然从 ON 转变为 OFF 或反之，通常需要复杂的药物治疗方案。目前，量化 PD 患者运动症状的金标准是评估统一帕金森病评定量表（UPDRS），但该量表繁琐，可能依赖于研究人员。本研究旨在开发一种用于 PD 患者运动症状的移动、客观和非侵入性的测量方法。
使用附着在手环上的表面肌电图（sEMG）电极记录 45 名 PD 患者的数据。运动范式包括在使用和不使用多巴胺能药物的情况下进行的敲击任务。我们的目标是使用不同的回归模型和机器学习技术从 sEMG 特征预测 UPDRS 评分。
随机森林回归模型优于其他回归模型，真实和预测的 UPDRS 值之间的相关性为 0.739。
可以从 PD 患者在简单敲击任务期间的 sEMG 数据中推断出其运动异常。在未来，这样的记录可以帮助确定远程医疗应用中药物治疗变化的需求。
我们的研究结果支持可穿戴设备用于检测帕金森症状的实用性，并有助于未来开发个性化治疗方法。
