Automatic Detection of Parkinson Speech Under Noisy Environment

Abstract

This work primarily aims to automatically detect patients who are suffering from Parkinson’s disease (PD) in comparison to the individuals who are healthy, through voice samples under clean and different noisy environmental conditions. The dataset was subjected to colored noises, electronic noises and natural noise. A feature vector comprising seven mean spectral features and two mean temporal features have been extracted. The performance of the PD detection model, configured by different classifiers of K- nearest neighbor (KNN), Extreme Gradient Boost, and Classification and Regression Trees (CART) have been analyzed under varying noisy environments. The proposed model for PD detection offers 97.01% accuracy for noise free dataset with KNN classifier and it also performs optimally even in the presence of varying noises. All colored noise samples gave superior classification accuracy with KNN classifier and all electronic and natural noises gave best accuracy with Extreme Gradient Boost classifier.KeywordsParkinson’s diseaseSpeechNoiseK- nearest neighbors (KNN)eXtreme gradient boost (XGBoost)Classification and regression trees