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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 27th European Signal Processing Conference (EUSIPCO)

ISBN

978-9-0827-9703-9

ISSN

2076-1465

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

A Coruña

Datum konání akce

2. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000604567700409