Detail publikace

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

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

Jazyk

en

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.

Dokumenty

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"
}