Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F18%3A00108296" target="_blank" >RIV/00216224:14740/18:00108296 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICUMT.2018.8631265" target="_blank" >http://dx.doi.org/10.1109/ICUMT.2018.8631265</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT.2018.8631265" target="_blank" >10.1109/ICUMT.2018.8631265</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting

Popis výsledku v původním jazyce

Parkinson's disease (PD) is one of the most frequent neurodegenerative disorder with progressive decline in several motor and non-motor skills. Due to time-consuming and partially subjective conventional PD diagnosis, several more effective approaches based on signal processing and machine learning, e.g. online handwriting analysis, have been proposed. This paper introduces a new methodology of PD dysgraphia analysis based on fractional derivatives applied in PD handwriting quantification. The proposed methodology was evaluated on a database that consists 33 PD patients and 36 healthy controls who performed several handwriting tasks. Employing random forests classifier in combination with 5 kinematic features based on fractionalorder derivatives we reached 90% classification accuracy, 89% sensitivity, and 91% specificity. In comparison with the results of other related works dealing with the same database, the proposed approach brings improvements in PD dysgraphia diagnosis and confirms the impact of fractional derivatives in kinematic analysis.

Název v anglickém jazyce

Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting

Popis výsledku anglicky

Parkinson's disease (PD) is one of the most frequent neurodegenerative disorder with progressive decline in several motor and non-motor skills. Due to time-consuming and partially subjective conventional PD diagnosis, several more effective approaches based on signal processing and machine learning, e.g. online handwriting analysis, have been proposed. This paper introduces a new methodology of PD dysgraphia analysis based on fractional derivatives applied in PD handwriting quantification. The proposed methodology was evaluated on a database that consists 33 PD patients and 36 healthy controls who performed several handwriting tasks. Employing random forests classifier in combination with 5 kinematic features based on fractionalorder derivatives we reached 90% classification accuracy, 89% sensitivity, and 91% specificity. In comparison with the results of other related works dealing with the same database, the proposed approach brings improvements in PD dysgraphia diagnosis and confirms the impact of fractional derivatives in kinematic analysis.

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

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

2018 10TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT 2018): EMERGING TECHNOLOGIES FOR CONNECTED SOCIETY

ISBN

9781538693605

ISSN

2157-0221

e-ISSN

—

Počet stran výsledku

6

Strana od-do

„IEEE,IEEEReg8“-„5“

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Moscow, RUSSIA

Datum konání akce

5. 11. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000459238500067