Exploring robust computer-aided diagnosis of Parkinson’s disease based on various voice signals

Abstract

As the voice disorder is a typical early symptom of Parkinson, some researchers attempt to diagnose this disease based on voice data collected from suspected patients. Although existing methods can provide acceptable results, they just work in partial scenarios. In other words, they are not generable and robust enough. To this end, we present a Parkinson’s auxiliary diagnosis system based on human speech, which can adaptively build a suitable deep neural network based on sound features. The system includes two modules: hybrid features extraction and adaptive network construction. We extract kinds of information from the voice data to form a new compound feature. Furthermore, particle swarm optimization (PSO) algorithm is employed to build the corresponding 1D convolution network for features classification. Extensive experiments on two datasets consisting of English and Italian are conducted for evaluation purposes. Experimental results show that our method improves the accuracy of voice-based Parkinson’s disease detection to some extent.