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.

1st Department of Neurology Masaryk University and St Anne's Hospital Pekarska 664 656 91 Brno Czech Republic

Brno University of Technology Technicka 12 Brno Czech Republic

Tecnocampus Av Ernest Lluch 32 08302 Mataro Barcelona Spain

References provided by Crossref.org

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

Shannon entropy: A novel parameter for quantifying pentagon copying performance in non-demented Parkinson's disease patients