An Automatic Parkinson 's Disease Recognition System Based on Multi - Feature Selection of Motion Signals

Parkinson's disease (PD) seriously affects human health so it has wide application value for its automatic diagnosis. In this study, 5 wearable inertial sensors were used in acquiring the acceleration and angular velocity signals under 4 paradigm actions. Total of 27 features were extracted from the signals, including amplitude, frequency, fatigue degree, self similarity, cross correlation and approximate entropy of an action. Genetic algorithm and BP neural network was used for feature selection and data classification. The experiment data were acquired from 10 PD patients and 10 healthy subjects. The results showed that the classification efficiency was improved after feature selection, and the average sensitivity, specificity and accuracy of the classification were 87%, 100% and 93% respectively. It may have certain application value in computer aided diagnosis of Parkinson's disease.