NEW METHODOLOGY OF PARKINSONIC DYSGRAPHIA ANALYSIS BY ONLINE HANDWRITING USING FRACTIONAL DERIVATIVES

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127717" target="_blank" >RIV/00216305:26220/18:PU127717 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf" target="_blank" >http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

NEW METHODOLOGY OF PARKINSONIC DYSGRAPHIA ANALYSIS BY ONLINE HANDWRITING USING FRACTIONAL DERIVATIVES

Popis výsledku v původním jazyce

Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose, 37 PD patients and 38 healthy controls were enrolled. In comparison to results reported in other works, we proved that FDE in online handwriting analysis brings promising improvements. The best result of multivariate analysis was achieved with 83:89% classification accuracy in combination with 5 features using only one handwriting task (overlapped circles). This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia.

Název v anglickém jazyce

NEW METHODOLOGY OF PARKINSONIC DYSGRAPHIA ANALYSIS BY ONLINE HANDWRITING USING FRACTIONAL DERIVATIVES

Popis výsledku anglicky

Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose, 37 PD patients and 38 healthy controls were enrolled. In comparison to results reported in other works, we proved that FDE in online handwriting analysis brings promising improvements. The best result of multivariate analysis was achieved with 83:89% classification accuracy in combination with 5 features using only one handwriting task (overlapped circles). This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia.

Druh

D - Stať ve sborníku

CEP obor

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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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 24nd Conference STUDENT EEICT 2018

ISBN

978-80-214-5614-3

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

398-402

Název nakladatele

Neuveden

Místo vydání

BRNO

Místo konání akce

Brno

Datum konání akce

26. 4. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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