Parkinson’s Diagnosis through Compressed Speech Signals at remote Locations

Abstract

Rapid increase in Parkinson’s disease all over the world before the age of 60’s is alarming. Parkinson’s affects the neurological controls of human body. In this paper we intend to adopt machine based learning to automate the diagnosis of Parkinson’s disease with help of compressed speech signals sent via network. An advanced Solution is proposed to automate the diagnosis process more rapid than that of ordinary process. In this study Speech samples from 55 subjects have been collected, 43 with Parkinson Disease and 12 Healthy subjects. Speech signals from clients captured from various sensors and devices transmitted to the cloud for processing. In the cloud Speech samples are compressed using compression codecs MP3, MP4, G.722, G.226, GSM-EFR, AMR-WB, SVOPC/SILK, and OPUS, and then same are diagnosed without compression impact and deliver accurate results. Three classifiers SVM (Support Vector Machine), NN (Neural Network), and GA (Genetic Algorithm) are applied and calculated accurate results as compared with compressed and un-compressed voice samples. Successful experiments enabled us to achieve 90.7% accuracy. Same process can be used for online and social media applications which are available in portable devices as well.