2014
Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease
DROTÁR, Peter, Jiří MEKYSKA, Irena REKTOROVÁ, Lucia MASÁROVÁ, Zdeněk SMÉKAL et. al.Základní údaje
Originální název
Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease
Autoři
DROTÁR, Peter (703 Slovensko), Jiří MEKYSKA (203 Česká republika), Irena REKTOROVÁ (203 Česká republika, garant, domácí), Lucia MASÁROVÁ (703 Slovensko, domácí), Zdeněk SMÉKAL (203 Česká republika) a Marcos FAUNDEZ-ZANUY (724 Španělsko)
Vydání
Computer Methods and Programs in Biomedicine, Clare, Elsevier, 2014, 0169-2607
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
30000 3. Medical and Health Sciences
Stát vydavatele
Irsko
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 1.897
Kód RIV
RIV/00216224:14110/14:00078537
Organizační jednotka
Lékařská fakulta
UT WoS
000344937800001
Klíčová slova anglicky
Handwriting; Disease classification; Parkinson's disease; Micrographia; In-air movement; Decision support systems
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 26. 1. 2015 17:58, Soňa Böhmová
Anotace
V originále
Background and objective: Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. Methods: We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. Results: By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. Conclusions: Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.
Návaznosti
ED1.1.00/02.0068, projekt VaV |
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NT13499, projekt VaV |
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