Exploration of Various Fractional Order Derivatives in Parkinson's Disease Dysgraphia Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F22%3A00132034" target="_blank" >RIV/00216224:14110/22:00132034 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-19745-1_23" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-19745-1_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-19745-1_23" target="_blank" >10.1007/978-3-031-19745-1_23</a>

Result language

angličtina

Original language name

Exploration of Various Fractional Order Derivatives in Parkinson's Disease Dysgraphia Analysis

Original language description

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: Grunwald-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%.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

30210 - Clinical neurology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Intertwining Graphonomics with Human Movements

ISBN

9783031197444

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

14

Pages from-to

308-321

Publisher name

SPRINGER INTERNATIONAL PUBLISHING AG

Place of publication

CHAM

Event location

Las Palmas de Gran Canaria

Event date

Sep 7, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000913319000023