Detail publikace

Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features

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

Originální název

Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Parkinson’s disease (PD) is a common neurodegenerative disorder with prevalence rate estimated to 1.5% for people age over 65 years. The majority of PD patients is associated with handwriting abnormalities called PD dysgraphia, which is linked with rigidity and bradykinesia of muscles involved in the handwriting process. One of the effective approaches of quantitative PD dysgraphia analysis is based on online handwriting processing. In the frame of this study we aim to deeply evaluate and optimize advanced PD handwriting quantification based on fractional order derivatives (FD). For this purpose, we used 37 PD patients and 38 healthy controls from the PaHaW (PD handwriting database). The FD based features were employed in classification and regression analysis (using gradient boosted trees), and evaluated in terms of their discrimination power and abilities to assess severity of PD. The results suggest that the most discriminative and descriptive information provide FD based features extracted from a repetitive loop task or a sentence copy task (maximum sensitivity/specificity = 76 %, error in severity assessment = 14 %, error in PD duration estimation = 22 %). Next, we identified two optimal ranges for the order of fractional derivative, a = 0.05 – 0.45 and a = 0.65 – 0.80. Finally, we observed that inclusion of pressure, azimuth, and tilt together with kinematic features into mathematical modeling has no influence (positive or negative) on classification performance, however, there was a notable improvement in the estimation of PD duration.

Klíčová slova

online handwriting; Parkinson’s disease; dysgraphia; fractal calculus; fractional derivatives; classification; regression

Autoři

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

Vydáno

2. 9. 2019

Nakladatel

IEEE

Místo

New York

ISBN

978-9-0827-9703-9

Kniha

2019 27th European Signal Processing Conference (EUSIPCO)

ISSN

2076-1465

Periodikum

18th European Signal Processing Conference (EUSIPCO-2010)

Stát

Dánské království

Strany od

1

Strany do

5

Strany počet

5

URL

https://ieeexplore.ieee.org/document/8903088

BibTex

@inproceedings{BUT158110,
  author="Ján {Mucha} and Marcos {Faúndez Zanuy} and Jiří {Mekyska} and Vojtěch {Zvončák} and Zoltán {Galáž} and Tomáš {Kiska} and Zdeněk {Smékal} and Luboš {Brabenec} and Irena {Rektorová} and Karmele {Lopez-de-Ipina}",
  title="Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features",
  booktitle="2019 27th European Signal Processing Conference (EUSIPCO)",
  year="2019",
  journal="18th European Signal Processing Conference (EUSIPCO-2010)",
  pages="1--5",
  publisher="IEEE",
  address="New York",
  doi="10.23919/EUSIPCO.2019.8903088",
  isbn="978-9-0827-9703-9",
  issn="2076-1465",
  url="https://ieeexplore.ieee.org/document/8903088"
}