Automated analysis of Parkinson’s Disease on the basis of evaluation of handwriting

In this thesis current state-of-the-art methods of automatic analysis of Parkinson’s disease (PD) are tested along with new ideas of signal processing. Since there is currently no cure for PD, it is important to introduce methods for automatic monitoring and analysis. Therefore handwriting-samples of 49 healthy subjects and 75 PD patients acquired with a graphic tablet are used. Those subjects performed different drawing tasks. With a kinematic analysis
accuracies of up 77% are achieved when using one task alone and accuracies up to 86% are achieved when combining different tasks. A newly developed spectral analysis resulted in scores of up to 96% for an individual task. Combining the spectral features of a standalone task with features from different tasks or a different analysis did not lead to better results. Making predictions about the severity of the disease based on the features acquired for the bi-class problem failed. An attempt was made modeling the velocity profile of strokes with lognormal distributions and using the thereby obtained parameters for classification. Because of difficulties with the modeling of strokes with different lengths, a classification failed.