Detail publikačního výsledku

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

MUCHA, J.; ZVONČÁK, V.; GALÁŽ, Z.; MEKYSKA, J.; KISKA, T.; SMÉKAL, Z.; FAÚNDEZ ZANUY, M.; BRABENEC, L.; REKTOROVÁ, I.; LOPEZ-DE-IPINA, K.

Originální název

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

Anglický název

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

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, 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.

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, 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.

Klíčová slova

Archimedean spiral; binary classification; fractal calculus; fractional derivative; online handwriting; Parkinson’s disease;

Klíčová slova v angličtině

Archimedean spiral; binary classification; fractal calculus; fractional derivative; online handwriting; Parkinson’s disease;

Autoři

MUCHA, J.; ZVONČÁK, V.; GALÁŽ, Z.; MEKYSKA, J.; KISKA, T.; SMÉKAL, Z.; FAÚNDEZ ZANUY, M.; BRABENEC, L.; REKTOROVÁ, I.; LOPEZ-DE-IPINA, K.

Rok RIV

2019

Vydáno

04.06.2018

Místo

Atény, Řecko

ISBN

978-1-5386-4695-3

Kniha

41st International Conference on Telecommunications and Signal Processing (TSP)

Strany od

214

Strany do

217

Strany počet

4

URL

https://ieeexplore.ieee.org/document/8441293/

BibTex

@inproceedings{BUT148762,
  author="Ján {Mucha} and Vojtěch {Zvončák} and Zoltán {Galáž} and Jiří {Mekyska} and Tomáš {Kiska} and Zdeněk {Smékal} and Marcos {Faúndez Zanuy} and Luboš {Brabenec} and Irena {Rektorová} and Karmele {Lopez-de-Ipina}",
  title="Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis",
  booktitle="41st International Conference on Telecommunications and Signal Processing (TSP)",
  year="2018",
  pages="214--217",
  address="Atény, Řecko",
  doi="10.1109/TSP.2018.8441293",
  isbn="978-1-5386-4695-3",
  url="https://ieeexplore.ieee.org/document/8441293/"
}