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

D 2018

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

MUCHA, J., V. ZVONCAK, Zoltán GALÁŽ, M. FAUNDEZ-ZANUY, J. MEKYSKA et. al.

Základní údaje

Originální název

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

Autoři

MUCHA, J., V. ZVONCAK, Zoltán GALÁŽ (703 Slovensko, domácí), M. FAUNDEZ-ZANUY, J. MEKYSKA, T. KISKA, Z. SMEKAL, Luboš BRABENEC (203 Česká republika, domácí), Irena REKTOROVÁ (203 Česká republika, garant, domácí) a K. LOPEZ-DE-IPINA

Vydání

NEW YORK, 2018 41ST INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), od s. 214-217, 4 s. 2018

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

30103 Neurosciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14740/18:00108297

Organizační jednotka

Středoevropský technologický institut

ISBN

978-1-5386-4695-3

DOI

http://dx.doi.org/10.1109/TSP.2018.8441293

UT WoS

000454845100050

Klíčová slova anglicky

Archimedean spiral; binary classification; fractal calculus; fractional derivative; online handwriting; Parkinson's disease

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 29. 4. 2020 11:49, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

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.

Návaznosti

GA18-16835S, projekt VaV

Název: Výzkum pokročilých metod diagnózy a hodnocení vývojové dysgrafie založených na kvantitativní analýze online písma a kresby (Akronym: DiagnosisDysgraphia)

Investor: Grantová agentura ČR, Výzkum pokročilých metod diagnózy a hodnocení vývojové dysgrafie založených na kvantitativní analýze online písma a kresby

NV16-30805A, projekt VaV

Název: Efekt neinvazivní stimulace mozku na hypokinetickou dysartrii, mikrografii a mozkovou plasticitu u pacientů s Parkinsonovou nemocí

Citovat

MUCHA, J., V. ZVONCAK, Zoltán GALÁŽ, M. FAUNDEZ-ZANUY, J. MEKYSKA, T. KISKA, Z. SMEKAL, Luboš BRABENEC, Irena REKTOROVÁ a K. LOPEZ-DE-IPINA. Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis. Online. In 2018 41ST INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP). NEW YORK: IEEE, 2018, s. 214-217. ISBN 978-1-5386-4695-3. Dostupné z: https://dx.doi.org/10.1109/TSP.2018.8441293.

@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 a 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, s.~214-217. ISBN~978-1-5386-4695-3. Dostupné z: https://dx.doi.org/10.1109/TSP.2018.8441293.

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