Fractional Derivatives of Online Handwriting: A New Approach of Parkinsonic Dysgraphia Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F18%3A00069061" target="_blank" >RIV/00159816:_____/18:00069061 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/18:PU128391 RIV/00216224:14740/18:00108297

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8441293" target="_blank" >https://ieeexplore.ieee.org/document/8441293</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2018.8441293" target="_blank" >10.1109/TSP.2018.8441293</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fractional Derivatives of Online Handwriting: A New Approach of Parkinsonic Dysgraphia Analysis

Popis výsledku v původním jazyce

Parkinson&apos;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, 30 PD patients and 36 healthy controls were enrolled. In comparison with results reported in other works, the newly designed features based on fractional derivatives increased classification accuracy by 8 % in univariate analysis and by 10 % when employing the multivariate one. This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia.

Název v anglickém jazyce

Fractional Derivatives of Online Handwriting: A New Approach of Parkinsonic Dysgraphia Analysis

Popis výsledku anglicky

Parkinson&apos;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, 30 PD patients and 36 healthy controls were enrolled. In comparison with results reported in other works, the newly designed features based on fractional derivatives increased classification accuracy by 8 % in univariate analysis and by 10 % when employing the multivariate one. This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

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

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Applied Sciences -Spec. issue

ISBN

978-1-5386-4695-3

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

214-217

Název nakladatele

IEEE

Místo vydání

Athens, Greece

Místo konání akce

Athens, Greece

Datum konání akce

4. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—