Automatic Analysis of Arabic Online Handwriting of Patients with Parkinson's Disease

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 usually also 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. Online 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 18 Parkinson's disease (PD) patients and 18 age matched healthy controls. We focused on copying an Arabic text task. For each participant we have calculated 528 features, and the purpose of this study is to find a subset of selected handwriting features suitable for efficiently identifying subjects with PD. The selected features were fed to a support vector machine classifier with RBF kernel, whose aim is to identify the subjects suffering from PD. Robustness of this classifier is measured with three performance matrices i.e., accuracy, sensitivity, and specificity. The obtained results show almost 80% overall classification accuracy.