Publication detail

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

MUCHA, J.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

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

Released

26.04.2018

Location

BRNO

ISBN

978-80-214-5614-3

Book

Proceedings of the 24nd Conference STUDENT EEICT 2018

Pages from

398

Pages to

402

Pages count

5

URL

BibTex


@inproceedings{BUT147110,
  author="Ján {Mucha}",
  title="NEW METHODOLOGY OF PARKINSONIC DYSGRAPHIA ANALYSIS BY ONLINE HANDWRITING USING FRACTIONAL DERIVATIVES",
  annote="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.",
  booktitle="Proceedings of the 24nd Conference STUDENT EEICT 2018",
  chapter="147110",
  howpublished="online",
  year="2018",
  month="april",
  pages="398--402",
  type="conference paper"
}