Parkinson’s Disease Diagnosis using Spiral Test on Digital Tablets

Abstract

For a proper diagnosis, Parkinson's disease (PD) requires frequent visits to the doctor for physical tests, causing a huge burden on the patient. As PD impairs the handwriting ability, the handwriting pattern can be used as an indicator for PD diagnosis. More specifically, the Static Spiral Test (SST) and the Dynamic Spiral Test (DST), that consists in retracing spirals using digital pen. Such exam can be self-conducted by the patient, and thus it would be convenient and non-time-consuming for both the patient and the medical staff. In this project, we designed and implemented a system that automatically self-aiddiagnoses PD using SST and DST on digital tablets. The system includes two main components, image processing techniques to pre-process and extract the appropriate visual features and machine learning techniques to recognize PD automatically. The conducted experiment showed that the semi-local Edge Histogram Descriptor extracted from DST drawing, and conveyed to a Gaussian Kernel Support Vector Machine outperforms the other considered systems with an accuracy, specificity and sensitivity around 90%.

A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

“…Based on the impaired handwritten ability of PD patients, Al-Yousef et al (2020) applied the static spiral test and a dynamic spirals test performed on a digital tablet for the diagnosis of PD. The semi-local edge histogram extracted from the dynamic spiral test, and conveyed to a Gaussian kernel SVM presented a better result than other systems considered in the study, achieving specificity and accuracy of about 90%.…”