Decision support framework for Parkinsons disease based on novel handwriting markers

journal article in Web of Science

English

Parkinsons 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

Parkinsons disease, handwriting, tablet, decision support system, biomarker

DROTÁR, P.; MEKYSKA, J.; REKTOROVÁ, I.; MASAROVÁ, L.; SMÉKAL, Z.; FAÚNDEZ ZANUY, M.

2014

8. 5. 2015

IEEE

USA

1534-4320

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

23

3

United States of America

508

516

9