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%.Comment: Print ISBN 978-3-031-19744-