Advanced Parkinson's Disease Dysgraphia Analysis Based on
Fractional Derivatives of Online Handwriting

D 2018

Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting

MUCHA, J.; J. MEKYSKA; M. FAUNDEZ-ZANUY; K. LOPEZ-DE-IPINA; V. ZVONCAK et al.

Základní údaje

Originální název

Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting

Autoři

MUCHA, J.; J. MEKYSKA; M. FAUNDEZ-ZANUY; K. LOPEZ-DE-IPINA; V. ZVONCAK; Zoltán GALÁŽ; T. KISKA; Z. SMEKAL; Luboš BRABENEC a Irena REKTOROVÁ

Vydání

NEW YORK, 2018 10TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT 2018): EMERGING TECHNOLOGIES FOR CONNECTED SOCIETY, od s. "IEEE, IEEE Reg 8"-"5", 6 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"

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14740/18:00108296

Organizační jednotka

Středoevropský technologický institut

ISBN

978-1-5386-9360-5

ISSN

Klíčová slova anglicky

kinematic analysis; fractal calculus; fractional derivative; online handwriting; Parkinson's disease; Parkinson's disease dysgraphia

Štítky

rivok

Změněno: 29. 4. 2020 11:48, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

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.

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.; J. MEKYSKA; M. FAUNDEZ-ZANUY; K. LOPEZ-DE-IPINA; V. ZVONCAK; Zoltán GALÁŽ; T. KISKA; Z. SMEKAL; Luboš BRABENEC a Irena REKTOROVÁ. Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting. Online. In 2018 10TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT 2018): EMERGING TECHNOLOGIES FOR CONNECTED SOCIETY. NEW YORK: IEEE, 2018, s. "IEEE, IEEE Reg 8"-"5", 6 s. ISBN 978-1-5386-9360-5. Dostupné z: https://doi.org/10.1109/ICUMT.2018.8631265.

@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 = {https://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  - CONF
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 a 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, s.~''IEEE, IEEE Reg 8''-''5'', 6 s. ISBN~978-1-5386-9360-5. Dostupné z: https://doi.org/10.1109/ICUMT.2018.8631265.

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