An Efficient Real-Time Approach for Detection of Parkinson’s Disease

Abstract

Parkinson’s disease is one of the most complex neurological disorders which have affected mankind since ages. Recent studies in the field of Biomedical Engineering have shown that by analyzing the Verbal Response of any human being, it is highly feasible to predict the odds of having the deadly disease. A simple analysis of an utterance of “ahh” sound by a person can help to analyze the person’s state of neurological health from a layman’s perspective. The paper initially utilizes the SVM (Support Vector Machine) Learning algorithm to predict the odds of having the Parkinson’s disease from a variety of audio samples consisting of healthy and unhealthy population. The cepstral features are used to develop a Real-Time Program for user-friendly application which asks the user to utter “ahh” for as long and as boldly as possible and finally displays whether the user has Parkinson’s Disease or not. The Real-Time Program can prove to be a helpful tool for the people as well as the medical community in general, assisting in early diagnosis of the Parkinson’s disease.