Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU133018" target="_blank" >RIV/00216305:26220/19:PU133018 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8903088" target="_blank" >https://ieeexplore.ieee.org/document/8903088</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/EUSIPCO.2019.8903088" target="_blank" >10.23919/EUSIPCO.2019.8903088</a>

Result language

angličtina

Original language name

Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features

Original language description

Parkinson’s disease (PD) is a common neurodegenerative disorder with prevalence rate estimated to 1.5% for people age over 65 years. The majority of PD patients is associated with handwriting abnormalities called PD dysgraphia, which is linked with rigidity and bradykinesia of muscles involved in the handwriting process. One of the effective approaches of quantitative PD dysgraphia analysis is based on online handwriting processing. In the frame of this study we aim to deeply evaluate and optimize advanced PD handwriting quantification based on fractional order derivatives (FD). For this purpose, we used 37 PD patients and 38 healthy controls from the PaHaW (PD handwriting database). The FD based features were employed in classification and regression analysis (using gradient boosted trees), and evaluated in terms of their discrimination power and abilities to assess severity of PD. The results suggest that the most discriminative and descriptive information provide FD based features extracted from a repetitive loop task or a sentence copy task (maximum sensitivity/specificity = 76 %, error in severity assessment = 14 %, error in PD duration estimation = 22 %). Next, we identified two optimal ranges for the order of fractional derivative, a = 0.05 – 0.45 and a = 0.65 – 0.80. Finally, we observed that inclusion of pressure, azimuth, and tilt together with kinematic features into mathematical modeling has no influence (positive or negative) on classification performance, however, there was a notable improvement in the estimation of PD duration.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

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

2019

Confidentiality

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

Article name in the collection

2019 27th European Signal Processing Conference (EUSIPCO)

ISBN

978-9-0827-9703-9

ISSN

2076-1465

e-ISSN

—

Number of pages

5

Pages from-to

1-5

Publisher name

IEEE

Place of publication

New York

Event location

A Coruña

Event date

Sep 2, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000604567700409