Publication detail

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.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

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

Released

02.09.2019

Location

A Coruňa, Španělsko

ISBN

978-9-0827-9703-9

Book

2019 27th European Signal Processing Conference (EUSIPCO)

Pages from

1

Pages to

5

Pages count

5

URL

Documents

BibTex


@inproceedings{BUT158110,
  author="Ján {Mucha} and Jiří {Mekyska} and Vojtěch {Zvončák} and Zoltán {Galáž}",
  title="Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features",
  annote="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.",
  booktitle="2019 27th European Signal Processing Conference (EUSIPCO)",
  chapter="158110",
  doi="10.23919/EUSIPCO.2019.8903088",
  howpublished="online",
  year="2019",
  month="september",
  pages="1--5",
  type="conference paper"
}