Voiced Features and Artificial Neural Network to Diagnose Parkinson’s Disease Patients

This paper presents an algorithm to detect Parkinson’s disease using voiced features and an artificial neural network (ANN). It uses 44 audio features extracted from the sustained vowel’/a/’ sound of the Parkinson’s disease patients and healthy subjects. To fully characterize a healthy and a Parkinson’s patient’s voice, audio features of different domains have been investigated. A simple two-layer feed-forward neural network (FFNN) has been deployed to avoid an overwhelming computational burden on the system. Significant statistical parameters are measured to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm achieves an accuracy of 85% and G-mean of 85.19% in identifying Parkinson’s patients.