Parkinson's disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although the basic kinematic features were proved to effectively quantify the symptoms of PD dysgraphia, a recent research identified that the theory of fractional calculus can be used to improve the graphomotor difficulties analysis. Therefore, in this study, we follow up on our previous research, and we aim to explore the utilization of various approaches of fractional order derivative (FD) in the analysis of PD dysgraphia. For this purpose, we used the repetitive loops task from the Parkinson's disease handwriting database (PaHaW). Handwritten signals were parametrized by the kinematic features employing three FD approximations: Gr\"unwald-Letnikov's, Riemann-Liouville's, and Caputo's. Results of the correlation analysis revealed a significant relationship between the clinical state and the handwriting features based on the velocity. The extracted features by Caputo's FD approximation outperformed the rest of the analyzed FD approaches. This was also confirmed by the results of the classification analysis, where the best model trained by Caputo's handwriting features resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78% and a specificity of 75.68%.

翻译：Parkinson病(PD)是一种常见的神经退化性障碍,65岁以上人群的流行率估计为2.0%。Parkinson病(PD)是一种常见的神经退化症(PD)。Parkinson病(PD)是一种常见的神经退化性障碍,65岁以上人群的流行率估计为2.0%。PD的红心运动运动运动症状,例如僵硬和胸膜骨质炎等,会影响笔迹过程的肌肉,导致笔迹异常。现在,通过对平面药片进行数字化分析而获得的在线手写信号(带有时间信息的信号)是最先进的方法。虽然基本运动特征已被证明可以有效地量化PDdysgraphia的感知性,但最近的一项研究发现,分数的微微计算学的理论可以用来改进笔迹分析。因此,我们在这项研究中,我们的目标是探索在分析PDDdy degragragraphia时,使用各种分序衍生法的信号。为此,我们使用了Parkinson病史分类数据库(PaHAW)的重复性循环任务(PAHA),最近一项研究发现手写信号信号信号的准确性信号的准确性,通过使用三种直径特征的直径的直径的直径的直径分析结果的直径的直径分析结果的直径。在三次的直径的直径的直径分析结果的直径分析中, 和直径性分析结果的直径法的直径的直径的直路的直径的直径的直径的直径的直径分析结果之间,这个分析结果的直径径径:一个主要的直径:一个主要的直径。