期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：5
DOI：10.14569/IJACSA.2020.0110560
出版社：Science and Information Society (SAI)
摘要：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-aid-diagnoses 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%.
