Peter Drotár1, Jiří Mekyska1, Irena Rektorová2, Lucia Masarová3, Zdenek Smékal1, Marcos Faundez-Zanuy4. 1. Brno University of Technology, Technicka 12, Brno, Czech Republic. 2. First Department of Neurology, Masaryk University and St. Anne's Hospital, Pekarska 664, 656 91 Brno, Czech Republic. Electronic address: irena.rektorova@fnusa.cz. 3. First Department of Neurology, Masaryk University and St. Anne's Hospital, Pekarska 664, 656 91 Brno, Czech Republic. 4. Tecnocampus, Av. Ernest Lluch, 32, 08302 Mataro, Barcelona, Spain.
Abstract
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.
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 PDpatients on medication and 38 age- and gender-matched healthy controls. RESULTS: By applying feature selection algorithms and support vector machine learning methods to separate PDpatients 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.
Authors: Zoltan Galaz; Peter Drotar; Jiri Mekyska; Matej Gazda; Jan Mucha; Vojtech Zvoncak; Zdenek Smekal; Marcos Faundez-Zanuy; Reinel Castrillon; Juan Rafael Orozco-Arroyave; Steven Rapcsak; Tamas Kincses; Lubos Brabenec; Irena Rektorova Journal: Front Neuroinform Date: 2022-05-30 Impact factor: 3.739
Authors: João W M de Souza; Shara S A Alves; Elizângela de S Rebouças; Jefferson S Almeida; Pedro P Rebouças Filho Journal: Comput Intell Neurosci Date: 2018-04-24