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@inproceedings{1649158,
author = {Mucha, J. and Mekyska, J. and FaundezandZanuy, M. and LopezanddeandIpina, K. and Zvoncak, V. and Galáž, Zoltán and Kiska, T. and Smekal, Z. and Brabenec, Luboš and Rektorová, Irena},
address = {NEW YORK},
booktitle = {2018 10TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT 2018): EMERGING TECHNOLOGIES FOR CONNECTED SOCIETY},
doi = {http://dx.doi.org/10.1109/ICUMT.2018.8631265},
keywords = {kinematic analysis; fractal calculus; fractional derivative; online handwriting; Parkinson's disease; Parkinson's disease dysgraphia},
howpublished = {elektronická verze "online"},
language = {eng},
location = {NEW YORK},
isbn = {978-1-5386-9360-5},
pages = {"IEEE, IEEE Reg 8"-"5"},
publisher = {IEEE},
title = {Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting},
year = {2018}
}
TY - JOUR
ID - 1649158
AU - Mucha, J. - Mekyska, J. - Faundez-Zanuy, M. - Lopez-de-Ipina, K. - Zvoncak, V. - Galáž, Zoltán - Kiska, T. - Smekal, Z. - Brabenec, Luboš - Rektorová, Irena
PY - 2018
TI - Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting
PB - IEEE
CY - NEW YORK
SN - 9781538693605
KW - kinematic analysis
KW - fractal calculus
KW - fractional derivative
KW - online handwriting
KW - Parkinson's disease
KW - Parkinson's disease dysgraphia
N2 - Parkinson's disease (PD) is one of the most frequent neurodegenerative disorder with progressive decline in several motor and non-motor skills. Due to time-consuming and partially subjective conventional PD diagnosis, several more effective approaches based on signal processing and machine learning, e.g. online handwriting analysis, have been proposed. This paper introduces a new methodology of PD dysgraphia analysis based on fractional derivatives applied in PD handwriting quantification. The proposed methodology was evaluated on a database that consists 33 PD patients and 36 healthy controls who performed several handwriting tasks. Employing random forests classifier in combination with 5 kinematic features based on fractionalorder derivatives we reached 90% classification accuracy, 89% sensitivity, and 91% specificity. In comparison with the results of other related works dealing with the same database, the proposed approach brings improvements in PD dysgraphia diagnosis and confirms the impact of fractional derivatives in kinematic analysis.
ER -
MUCHA, J., J. MEKYSKA, M. FAUNDEZ-ZANUY, K. LOPEZ-DE-IPINA, V. ZVONCAK, Zoltán GALÁŽ, T. KISKA, Z. SMEKAL, Luboš BRABENEC and Irena REKTOROVÁ. Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting. Online. In \textit{2018 10TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT 2018): EMERGING TECHNOLOGIES FOR CONNECTED SOCIETY}. NEW YORK: IEEE, 2018, p.~''IEEE, IEEE Reg 8''-''5'', 6 pp. ISBN~978-1-5386-9360-5. Available from: https://dx.doi.org/10.1109/ICUMT.2018.8631265.
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