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@article{1216669,
author = {Drotár, Peter and Mekyska, Jiří and Rektorová, Irena and Masárová, Lucia and Smékal, Zdeněk and FaundezandZanuy, Marcos},
article_location = {PISCATAWAY (USA)},
article_number = {3},
doi = {http://dx.doi.org/10.1109/TNSRE.2014.2359997},
keywords = {Parkinson’s disease; decision support system; handwriting},
language = {eng},
issn = {1534-4320},
journal = {IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING},
title = {Decision support framework for Parkinson's disease based on novel handwriting markers},
url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6910308},
volume = {23},
year = {2015}
}
TY - JOUR
ID - 1216669
AU - Drotár, Peter - Mekyska, Jiří - Rektorová, Irena - Masárová, Lucia - Smékal, Zdeněk - Faundez-Zanuy, Marcos
PY - 2015
TI - Decision support framework for Parkinson's disease based on novel handwriting markers
JF - IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
VL - 23
IS - 3
SP - 508-516
EP - 508-516
PB - IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
SN - 15344320
KW - Parkinson’s disease
KW - decision support system
KW - handwriting
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6910308
L2 - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6910308
N2 - Parkinson’s disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex- matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88:13%, with the highest values of sensitivity and specificity equal to 89:47% and 91:89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool.
ER -
DROTÁR, Peter, Jiří MEKYSKA, Irena REKTOROVÁ, Lucia MASÁROVÁ, Zdeněk SMÉKAL and Marcos FAUNDEZ-ZANUY. Decision support framework for Parkinson's disease based on novel handwriting markers. \textit{IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}. PISCATAWAY (USA): IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015, vol.~23, No~3, p.~508-516. ISSN~1534-4320. Available from: https://dx.doi.org/10.1109/TNSRE.2014.2359997.
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