## 1 Introduction
reported the results of a study focused only on the early stages of PD; the best accuracy was 74.76% for a combination of three handwriting tasks. Finally, in our previous work [20], we proposed a new approach of advanced kinematic feature extraction that utilizes fractional-order derivatives (FD). This approach increased the classification accuracy by 10% (72.39%) for Archimedean spiral tasks in comparison with the baseline [20]. \begin{table}
\begin{tabular}{l c c l l l l} \hline \hline
**First Author** & **Year** & **PD/HC** & **Handwriting Task** & **Analysis** & **Features** & **Conclusions** \\ \hline [PERSON] \({}^{*}\)[17] & 2015 & 37/38 & letters, words, sentences & differential analysis (SVM) & kinematic, temporal, spatial, entropy, EMD, signal energy & The highest classification accuracy after feature selection approach was 88.13\%. \\ \hline [PERSON] \({}^{*}\)[16] & 2015 & 37/38 & letters, words, sentences & differential analysis (SVM) & kinematic, temporal, spatial, entropy, EMD, pressure & Classification performance was as its peak with on-surface features equal to AUC = 89.09\%. \\ \hline [PERSON] [24] & 2015 & 34/10 & up/down strokes at varying amplitudes & ANOVA & spatial and kinematic spatial (\(F\)(2.41) = 3.97; \(p\) = 0.03). \\ \hline [PERSON] [25] & 2015 & 37/18 & Archimedean spiral & differential an. & mean relative tremor and spatial parameters & The best results were obtained by NB classifier and spatial parameters & that provided around 79\% classification accuracy. \\ \hline [PERSON] \({}^{*}\)[8] & 2016 & 37/38 & letters, words, Archimedean spiral, sentences & differential an. & kinematic, temporal, spatial, entropy, EMD, pressure & Combining all exercises, SVM proved to be the best classifier with 82.5\% accuracy. \\ \hline [PERSON] [26] & 2016 & 30/15 & repetitive cursive loops & ANOVA, correlation an. & writing amplitude and velocity & PD dysgraphia is more severe in patients with freezing of gait. \\ \hline [PERSON] [27] & 2016 & 14/21 & Archimedean spiral, meander & differential an. & \\  & & & & (CNN, OPF) & pen-based features & The best result was obtained by CNN with 87.14\% classification accuracy using meander task. \\ \hline [PERSON] [21] & 2017 & 24/20 & horizontal lines & differential analysis (NIB) & kinematic & Average classification accuracy was 91\%. \\ \hline [PERSON] [18] & 2017 & 4/7 & sentence, repetitive loops & differential analysis (ANN) & temporal, kinematic, spatial & Highest classification accuracy (96.81\%) was achieved using all the extracted features. \\ \hline [PERSON] [22] & 2017 & 16/16 & letters, waves, words & differential analysis (SVM) & kinematic, stroke, pressure, entropy, EMD & The highest classification accuracy was 96.88\% for 12 kinematic and pressure features. \\ \hline [PERSON] \({}^{*}\)[23] & 2018 & 37/38 & Archimedean spiral, letters, words, sentence, loops & differential analysis (SVM) & CNN-based features & Extraction of features using CNN applied on raw handwriting data resulted in 83\% classification accuracy. \\ \hline [PERSON] \({}^{*}\)[20] & 2018 & 30/36 & Archimedean spiral & differential & fractional derivatives based & Improvement of classification accuracy \\  & & & & & analysis (RF, SVM, K-NN, & kinematic features & by 10\% (72.38\%) in comparison to the baseline. \\ \hline [PERSON] \({}^{*}\)[28] & 2018 & 37/38 & Archimedean spiral & differential an. & differential an. & kinematic, temporal, spatial, entropy, EMD, pressure & Analysis focused on PD diagnosis at earlier stages \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of related works focused on computerized analysis of Parkinson’s disease (PD) dysgraphia. Although the authors of the previously mentioned studies reported high classification accuracies, further signal processing and machine learning pipeline improvements are expected to make the differential analysis even more accurate.