School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK.
ICON PLC, South County Business Park, Dublin 18, Ireland.
J Healthc Eng. 2021 Sep 10;2021:9624386. doi: 10.1155/2021/9624386. eCollection 2021.
Tremor is a common symptom of Parkinson's disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.
震颤是帕金森病（PD）的常见症状。目前，震颤是根据 MDS-UPDRS 评定量表进行临床评估的，但该量表不准确、主观且不可靠。准确评估震颤严重程度是有效治疗以缓解症状的关键。因此，已经提出了几种从患者执行脚本和非脚本任务时收集的数据中测量和量化 PD 震颤的客观方法。然而，到目前为止，文献似乎侧重于提出震颤严重程度分类方法，而没有区分任务对分类和震颤严重程度测量的影响。在这项研究中，使用了一种新方法来确定一个推荐系统，以测量震颤严重程度，包括在数据收集过程中执行的任务对分类性能的影响。推荐系统包括推荐任务、分类器、分类器超参数和重采样技术。该方法基于四个子数据集、六种重采样技术、六种分类器以及信号处理和特征提取技术的五个高级指标结果的平均规则之上。本研究的结果表明，不涉及直接手腕运动的任务比涉及直接手腕运动的任务更适合用于测量震颤严重程度。此外，重采样技术显著提高了分类性能。本研究的结果表明，推荐系统由支持向量机（SVM）分类器与 BorderlineSMOTE 过采样技术相结合，并在执行一系列推荐任务（包括坐着、上下楼梯、直走、计数时行走和站立）时进行数据收集。
