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@article{1367284,
author = {Drotár, Peter and Mekyska, Jiří and Rektorová, Irena and Masárová, Lucia and Smékal, Zdeněk and FaundezandZanuy, Marcos},
article_location = {Amsterdam},
article_number = {"neuvedeno"},
doi = {http://dx.doi.org/10.1016/j.artmed.2016.01.004},
keywords = {Decision support system; Support vector machine classifier; Handwriting database; Handwriting pressure; Parkinson's disease; PD dysgraphia},
language = {eng},
issn = {0933-3657},
journal = {Artificial Intelligence in Medicine},
title = {Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease},
volume = {67},
year = {2016}
}
TY - JOUR
ID - 1367284
AU - Drotár, Peter - Mekyska, Jiří - Rektorová, Irena - Masárová, Lucia - Smékal, Zdeněk - Faundez-Zanuy, Marcos
PY - 2016
TI - Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
JF - Artificial Intelligence in Medicine
VL - 67
IS - "neuvedeno"
SP - 39-46
EP - 39-46
PB - Elsevier Science BV
SN - 09333657
KW - Decision support system
KW - Support vector machine classifier
KW - Handwriting database
KW - Handwriting pressure
KW - Parkinson's disease
KW - PD dysgraphia
N2 - 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.
ER -
DROTÁR, Peter, Jiří MEKYSKA, Irena REKTOROVÁ, Lucia MASÁROVÁ, Zdeněk SMÉKAL and Marcos FAUNDEZ-ZANUY. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease. \textit{Artificial Intelligence in Medicine}. Amsterdam: Elsevier Science BV, 2016, vol.~67, ''neuvedeno'', p.~39-46. ISSN~0933-3657. Available from: https://dx.doi.org/10.1016/j.artmed.2016.01.004.
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