Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting
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,...
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doaj-31a0d730847b452a9056f7142fdfc7292020-11-24T22:51:59ZengMDPI AGApplied Sciences2076-34172018-12-01812256610.3390/app8122566app8122566Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online HandwritingJan Mucha0Jiri Mekyska1Zoltan Galaz2Marcos Faundez-Zanuy3Karmele Lopez-de-Ipina4Vojtech Zvoncak5Tomas Kiska6Zdenek Smekal7Lubos Brabenec8Irena Rektorova9Department of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech RepublicDepartment of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech RepublicDepartment of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech RepublicEscola Superior Politecnica, Tecnocampus Avda. Ernest Lluch 32, 08302 Mataro, Barcelona, SpainDepartment of Systems Engineering and Automation, University of the Basque Country UPV/EHU, Av de Tolosa 54, 20018 Donostia, SpainDepartment of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech RepublicDepartment of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech RepublicDepartment of Telecommunications and SIX Research Centre, Brno University of Technology, Technicka 10, 61600 Brno, Czech RepublicApplied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech RepublicApplied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech RepublicParkinson’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.https://www.mdpi.com/2076-3417/8/12/2566Parkinson’s disease dysgraphiamicrographiaonline handwritingkinematic analysisfractional-order derivativefractional calculus |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jan Mucha Jiri Mekyska Zoltan Galaz Marcos Faundez-Zanuy Karmele Lopez-de-Ipina Vojtech Zvoncak Tomas Kiska Zdenek Smekal Lubos Brabenec Irena Rektorova |
spellingShingle |
Jan Mucha Jiri Mekyska Zoltan Galaz Marcos Faundez-Zanuy Karmele Lopez-de-Ipina Vojtech Zvoncak Tomas Kiska Zdenek Smekal Lubos Brabenec Irena Rektorova Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting Applied Sciences Parkinson’s disease dysgraphia micrographia online handwriting kinematic analysis fractional-order derivative fractional calculus |
author_facet |
Jan Mucha Jiri Mekyska Zoltan Galaz Marcos Faundez-Zanuy Karmele Lopez-de-Ipina Vojtech Zvoncak Tomas Kiska Zdenek Smekal Lubos Brabenec Irena Rektorova |
author_sort |
Jan Mucha |
title |
Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting |
title_short |
Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting |
title_full |
Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting |
title_fullStr |
Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting |
title_full_unstemmed |
Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting |
title_sort |
identification and monitoring of parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2018-12-01 |
description |
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. |
topic |
Parkinson’s disease dysgraphia micrographia online handwriting kinematic analysis fractional-order derivative fractional calculus |
url |
https://www.mdpi.com/2076-3417/8/12/2566 |
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