DROTÁR, Peter, Jiří MEKYSKA, Irena REKTOROVÁ, Lucia MASÁROVÁ, Zdeněk SMÉKAL a Marcos FAUNDEZ-ZANUY. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease. Artificial Intelligence in Medicine, Amsterdam: Elsevier Science BV, 2016, roč. 67, "neuvedeno", s. 39-46. ISSN 0933-3657. doi:10.1016/j.artmed.2016.01.004.
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Základní údaje
Originální název Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
Autoři DROTÁR, Peter (203 Česká republika), Jiří MEKYSKA (203 Česká republika), Irena REKTOROVÁ (203 Česká republika, domácí), Lucia MASÁROVÁ (703 Slovensko), Zdeněk SMÉKAL (203 Česká republika) a Marcos FAUNDEZ-ZANUY (724 Španělsko).
Vydání Artificial Intelligence in Medicine, Amsterdam, Elsevier Science BV, 2016, 0933-3657.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 3. Medical and Health Sciences
Stát vydavatele Nizozemsko
Utajení není předmětem státního či obchodního tajemství
Impakt faktor Impact factor: 2.009
Kód RIV RIV/00216224:14110/16:00088939
Organizační jednotka Lékařská fakulta
Doi http://dx.doi.org/10.1016/j.artmed.2016.01.004
UT WoS 000374078900003
Klíčová slova anglicky Decision support system; Support vector machine classifier; Handwriting database; Handwriting pressure; Parkinson's disease; PD dysgraphia
Štítky EL OK
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Ing. Mgr. Věra Pospíšilíková, učo 9005. Změněno: 10. 1. 2017 15:35.
Anotace
Objective: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and material: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). Results: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of P-acc = 81.3% (sensitivity P-sen = 87.4% and specificity of P-spe = 80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding P-acc = 82.5% compared to P-acc = 75.4% using kinematic features. Conclusion: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.
Návaznosti
ED1.1.00/02.0068, projekt VaVNázev: CEITEC - central european institute of technology
NT13449, projekt VaVNázev: Spondylogenní komprese krční míchy - prevalence, diagnostika a prognóza
VytisknoutZobrazeno: 21. 10. 2019 02:55