Other formats:
BibTeX
LaTeX
RIS
@inproceedings{1649161,
author = {Mucha, J. and Zvoncak, V. and Galáž, Zoltán and FaundezandZanuy, M. and Mekyska, J. and Kiska, T. and Smekal, Z. and Brabenec, Luboš and Rektorová, Irena and LopezanddeandIpina, K.},
address = {NEW YORK},
booktitle = {2018 41ST INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)},
doi = {http://dx.doi.org/10.1109/TSP.2018.8441293},
keywords = {Archimedean spiral; binary classification; fractal calculus; fractional derivative; online handwriting; Parkinson's disease},
howpublished = {elektronická verze "online"},
language = {eng},
location = {NEW YORK},
isbn = {978-1-5386-4695-3},
pages = {214-217},
publisher = {IEEE},
title = {Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis},
year = {2018}
}
TY - JOUR
ID - 1649161
AU - Mucha, J. - Zvoncak, V. - Galáž, Zoltán - Faundez-Zanuy, M. - Mekyska, J. - Kiska, T. - Smekal, Z. - Brabenec, Luboš - Rektorová, Irena - Lopez-de-Ipina, K.
PY - 2018
TI - Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis
PB - IEEE
CY - NEW YORK
SN - 9781538646953
KW - Archimedean spiral
KW - binary classification
KW - fractal calculus
KW - fractional derivative
KW - online handwriting
KW - Parkinson's disease
N2 - 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.
ER -
MUCHA, J., V. ZVONCAK, Zoltán GALÁŽ, M. FAUNDEZ-ZANUY, J. MEKYSKA, T. KISKA, Z. SMEKAL, Luboš BRABENEC, Irena REKTOROVÁ and K. LOPEZ-DE-IPINA. Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis. Online. In \textit{2018 41ST INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)}. NEW YORK: IEEE, 2018, p.~214-217. ISBN~978-1-5386-4695-3. Available from: https://dx.doi.org/10.1109/TSP.2018.8441293.
|
