Identification and Monitoring of Parkinson’s Disease Dysgraphia
Based on Fractional-Order Derivatives of Online Handwriting

J 2018

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

MUCHA, Jan, Jiri MEKYSKA, Zoltán GALÁŽ, Marcos FAUNDEZ ZANUY, Karmele LOPEZ-DE-IPINA et. al.

Základní údaje

Originální název

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

Autoři

MUCHA, Jan (203 Česká republika), Jiri MEKYSKA (203 Česká republika), Zoltán GALÁŽ (203 Česká republika, domácí), Marcos FAUNDEZ ZANUY (724 Španělsko), Karmele LOPEZ-DE-IPINA (724 Španělsko), Vojtech ZVONCAK (203 Česká republika), Tomas KISKA (203 Česká republika), Zdenek SMEKAL (203 Česká republika), Luboš BRABENEC (203 Česká republika, domácí) a Irena REKTOROVÁ (203 Česká republika, garant, domácí)

Vydání

APPLIED SCIENCES, Basel, MDPI, 2018, 2076-3417

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30103 Neurosciences

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.217

Kód RIV

RIV/00216224:14740/18:00101570

Organizační jednotka

Středoevropský technologický institut

DOI

http://dx.doi.org/10.3390/app8122566

UT WoS

000455145000234

Klíčová slova anglicky

Parkinson’s disease dysgraphia; micrographia; online handwriting; kinematic analysis; fractional-order derivative; fractional calculus

Štítky

14110127, podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 18. 3. 2019 14:03, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.

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

734718, interní kód MU

Název: Novel Network-Based Approaches for Studying Cognitive Dysfunction in Behavioral Neurology (Akronym: CoBeN)

Investor: Evropská unie, Novel Network-Based Approaches for Studying Cognitive Dysfunction in Behavioral Neurology, MSCA Marie Skłodowska-Curie Actions (Excellent Science)

Přiložené soubory

applsci-08-02566.pdf

Vyhledat podobné dokumenty

Citovat

MUCHA, Jan, Jiri MEKYSKA, Zoltán GALÁŽ, Marcos FAUNDEZ ZANUY, Karmele LOPEZ-DE-IPINA, Vojtech ZVONCAK, Tomas KISKA, Zdenek SMEKAL, Luboš BRABENEC a Irena REKTOROVÁ. Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting. APPLIED SCIENCES. Basel: MDPI, 2018, roč. 8, č. 12, s. 1-18. ISSN 2076-3417. Dostupné z: https://dx.doi.org/10.3390/app8122566.

@article{1486841,
   author = {Mucha, Jan and Mekyska, Jiri and Galáž, Zoltán and Faundez Zanuy, Marcos and LopezanddeandIpina, Karmele and Zvoncak, Vojtech and Kiska, Tomas and Smekal, Zdenek and Brabenec, Luboš and Rektorová, Irena},
   article_location = {Basel},
   article_number = {12},
   doi = {http://dx.doi.org/10.3390/app8122566},
   keywords = {Parkinson’s disease dysgraphia; micrographia; online handwriting; kinematic analysis; fractional-order derivative; fractional calculus},
   language = {eng},
   issn = {2076-3417},
   journal = {APPLIED SCIENCES},
   title = {Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting},
   volume = {8},
   year = {2018}
}

TY  - JOUR
ID  - 1486841
AU  - Mucha, Jan - Mekyska, Jiri - Galáž, Zoltán - Faundez Zanuy, Marcos - Lopez-de-Ipina, Karmele - Zvoncak, Vojtech - Kiska, Tomas - Smekal, Zdenek - Brabenec, Luboš - Rektorová, Irena
PY  - 2018
TI  - Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting
JF  - APPLIED SCIENCES
VL  - 8
IS  - 12
SP  - 1-18
EP  - 1-18
PB  - MDPI
SN  - 20763417
KW  - Parkinson’s disease dysgraphia
KW  - micrographia
KW  - online handwriting
KW  - kinematic analysis
KW  - fractional-order derivative
KW  - fractional calculus
N2  - Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.
ER  -

MUCHA, Jan, Jiri MEKYSKA, Zoltán GALÁŽ, Marcos FAUNDEZ ZANUY, Karmele LOPEZ-DE-IPINA, Vojtech ZVONCAK, Tomas KISKA, Zdenek SMEKAL, Luboš BRABENEC a Irena REKTOROVÁ. Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting. \textit{APPLIED SCIENCES}. Basel: MDPI, 2018, roč.~8, č.~12, s.~1-18. ISSN~2076-3417. Dostupné z: https://dx.doi.org/10.3390/app8122566.

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