Assessing Visual Attributes of Handwriting for Prediction of Neurological Disorders - A Case Study on Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is a degenerative disorder that progressively affects the central nervous system causing muscle rigidity, tremors, slowed movements and impaired balance. Sophisticated diagnostic procedures like SPECT scans can detect changes in the brain caused by PD but are only effective once the disease has advanced considerably. Analysis of subtle variations in handwriting and speech can serve as potential tools for early prediction of the disease. While traditional techniques mostly rely on dynamic (kinematic and spatio-temporal) features of handwriting, in this study, we quantitatively evaluate the visual attributes in characterization of graphomotor samples of PD patients. For this purpose, Convolutional Neural Networks are employed to extract discriminating visual features from multiple representations of various graphomotor samples produced by both control and PD subjects. The extracted features are then fed to a Support Vector Machine (SVM) classifier. Evaluations are carried out on a dataset of 72 subjects using early and late fusion techniques and an overall accuracy of 83% is realized with solely visual information.