A new semi-supervised approach for characterizing the Arabic on-line handwriting of Parkinson's disease patients

Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and the first manifestation of PD is deterioration of handwriting. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. On-line handwriting analysis is one of the methods that can be used to diagnose PD. In this study, we aimed to analyze the Arabic Handwriting of 28 Parkinson's disease patients and 28 healthy controls (HCs) who were the same age and have the same intellectual level. We focused on copying an Arabic text task. For each participant we have calculated 1482 features. Based on the most relevant features selected by the Pearson's coefficient correlation, the Hierarchical Ascendant Classification (HAC) was applied and generated 3 clusters of writers. The characterization of these clusters was carried out by using the quantitative and qualitative parameters. The obtained results show that the combination of these two aspects can discriminate at best PD patients from HCs.