Feature extraction and classification of static spiral tests to assist the detection of Parkinson's disease

Analyses of spiral drawings have been carried out in clinics to study and assist the diagnosis of Parkinson's Disease (PD), the second most common neuro-degenerative disorder in people at their 60's. The purpose of this research is to propose an approach to classify static spiral drawings, as an assisting tool for PD diagnosis using a simple data set obtained from a balanced population of patients with PD and controls. In this study, analyses were conducted on pictures of drawings, an affordable technological application, specially in small clinics where neither resources, such as tablets, nor specialists are available. The most significant contribution of this work lies on the extraction and selection of features. Five feature groups are used to characterize the natural process of tracing a spiral for both PD patients and controls. These groups convey information related to the trace movement on the plane, trace pressure, texture, frequency content, and morphology. Furthermore, the number of features is reduced by searching for the best feature subset. Three classifiers are used: k-nearest neighbors, multi-layer perceptron, and support vector machine. The best detection performance achieved is 86.67% of accuracy, 80.00% of sensitivity, 100% of specificity, 100% of positive predictive value, and 82.35% of negative predictive value.