Detail publikace

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

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

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, 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.

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",
  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, 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.",
  booktitle="41st International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="148762",
  doi="10.1109/TSP.2018.8441293",
  howpublished="online",
  year="2018",
  month="june",
  pages="214--217",
  type="conference paper"
}