Lanzhou Jiaotong University, Lanzhou 730070, People's Republic of China.
Quanzhou Institute of Equipment Manufacturing, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Jinjiang 362216, People's Republic of China.
Physiol Meas. 2023 Feb 20;44(2). doi: 10.1088/1361-6579/acb8fe.
. The quantitative assessment of Parkinsonian tremor, e.g. (0, 1, 2, 3, 4) according to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale, is crucial for treating Parkinson's disease. However, the tremor amplitude constantly fluctuates due to environmental and psychological effects on the patient. In clinical practice, clinicians assess the tremor severity for a short duration, whereas manual tremor labeling relies on the clinician's physician experience. Therefore, automatic tremor quantification based on wearable inertial sensors and machine learning algorithms is affected by the manual labels of clinicians. In this study, an automatic modification method for the labels judged by clinicians is presented to improve Parkinsonian tremor quantitation.. For the severe overlapping of dynamic feature range between different severities, an outlier modification algorithm (PCA-IQR) based on the combination of principal component analysis and interquartile range statistic rule is proposed to learn the blurred borders between different severity scores, thereby optimizing the labels. Afterward, according to the modified feature vectors, a support vector machine (SVM) with a radial basis function (RBF) kernel is proposed to classify the tremor severity. The classifier models of SVM with RBF kernel,-nearest neighbors, and SVM with the linear kernel are compared.. Experimental results show that the proposed method has high classification performance and excellent model generalization ability for tremor quantitation (accuracy: 97.93%, precision: 97.96%, sensitivity: 97.93%, F1-score: 97.94%).. The proposed method may not only provide valuable assistance for clinicians to assess the tremor severity accurately, but also provides self-monitoring for patients at home and improve the assessment skills of clinicians.
. 帕金森震颤的定量评估，例如（0、1、2、3、4）根据运动障碍协会统一帕金森病评定量表，对治疗帕金森病至关重要。然而，由于环境和心理因素对患者的影响，震颤幅度不断波动。在临床实践中，临床医生会在短时间内评估震颤的严重程度，而手动震颤标记依赖于临床医生的医生经验。因此，基于可穿戴惯性传感器和机器学习算法的自动震颤量化受到临床医生手动标记的影响。在这项研究中，提出了一种自动修改临床医生判断的标签的方法，以提高帕金森震颤定量。对于不同严重程度之间动态特征范围的严重重叠，提出了一种基于主成分分析和四分位距统计规则相结合的异常值修改算法（PCA-IQR），以学习不同严重程度评分之间的模糊边界，从而优化标签。然后，根据修改后的特征向量，提出了一种基于径向基函数（RBF）核的支持向量机（SVM）来分类震颤严重程度。比较了 SVM 核的支持向量机、-最近邻和 SVM 核的线性核的分类器模型。实验结果表明，该方法对震颤定量具有较高的分类性能和出色的模型泛化能力（准确率：97.93%，精度：97.96%，灵敏度：97.93%，F1 得分：97.94%）。该方法不仅可以为临床医生准确评估震颤严重程度提供有价值的帮助，还可以为患者在家中提供自我监测，并提高临床医生的评估技能。
