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Open AccessArticle

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

1
Department of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
2
Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
3
Escola Superior Politecnica, Tecnocampus Avda. Ernest Lluch 32, 08302 Mataro, Barcelona, Spain
4
Department of Systems Engineering and Automation, University of the Basque Country UPV/EHU, Av de Tolosa 54, 20018 Donostia, Spain
5
First Department of Neurology, Masaryk University and St. Anne’s University Hospital, Pekarska 53, 65691 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Mucha, J.; Zvoncak, V.; Galaz, Z.; Faundez-Zanuy, M.; Mekyska, J.; Kiska, T.; Smekal, Z.; Brabenec, L.; Rektorova, I.; Ipina, K.L. Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis. In Proceedings of the 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, Greece, 4–6 July 2018; pp. 1–4.
Appl. Sci. 2018, 8(12), 2566; https://doi.org/10.3390/app8122566
Received: 30 October 2018 / Revised: 3 December 2018 / Accepted: 6 December 2018 / Published: 11 December 2018
Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment. View Full-Text
Keywords: Parkinson’s disease dysgraphia; micrographia; online handwriting; kinematic analysis; fractional-order derivative; fractional calculus Parkinson’s disease dysgraphia; micrographia; online handwriting; kinematic analysis; fractional-order derivative; fractional calculus
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Mucha, J.; Mekyska, J.; Galaz, Z.; Faundez-Zanuy, M.; Lopez-de-Ipina, K.; Zvoncak, V.; Kiska, T.; Smekal, Z.; Brabenec, L.; Rektorova, I. Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting. Appl. Sci. 2018, 8, 2566.

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