Detail publikačního výsledku

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

MUCHA, J.

Originální název

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

Anglický název

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

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Binary classification; fractal calculus; fractional derivative; online handwriting; overlapped circles; Parkinson’s disease

Klíčová slova v angličtině

Binary classification; fractal calculus; fractional derivative; online handwriting; overlapped circles; Parkinson’s disease

Autoři

MUCHA, J.

Rok RIV

2019

Vydáno

26.04.2018

Místo

BRNO

ISBN

978-80-214-5614-3

Kniha

Proceedings of the 24nd Conference STUDENT EEICT 2018

Strany od

398

Strany do

402

Strany počet

5

URL

http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf

BibTex

@inproceedings{BUT147110,
  author="Ján {Mucha}",
  title="NEW METHODOLOGY OF PARKINSONIC DYSGRAPHIA ANALYSIS BY ONLINE HANDWRITING USING FRACTIONAL DERIVATIVES",
  booktitle="Proceedings of the 24nd Conference STUDENT EEICT 2018",
  year="2018",
  pages="398--402",
  address="BRNO",
  isbn="978-80-214-5614-3",
  url="http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf"
}