Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

Taoufiq Belhoussine Drissi,Benayad Nsiri,Abdelkrim Ammoummou,Soumaya Zayrit

doi:10.14569/ijacsa.2019.0100315

Abstract

The aim of this study presented in this paper is to determine the choice of the appropriate wavelet analyzer with the method of extraction of MFCC coefficients for an assistance in the diagnosis of Parkinson's disease. The analysis used is based on a database of 18 healthy and 20 Parkinsonian patients. The suggested processing is based on the transformation of the speech signal by the wavelet transform through testing several sorts of wavelets, extracting Mel Frequency Cepstral Coefficients (MFCC) from the signals, and we apply the support vector machine (SVM) as classifier. The test results reveal that the best recognition rate, which is 86.84%, is obtained by the wavelets of level 2 at 3rd scale (Daubechie, Symlet, ReverseBior or BiorSpline) combination-MFCC–SVM.

Highlights

The history of Parkinson's disease began in 1817 by James Parkinson [1]
The suggested processing is based on the transformation of the speech signal by the wavelet transform through testing several sorts of wavelets, extracting Mel Frequency Cepstral Coefficients (MFCC) from the signals, and we apply the support vector machine (SVM) as classifier
We develop the diagnostic model of Parkinson's disease [12] by the introduction of vocal signal compression of a database [19], which is composed of 18 healthy patients and 20 patients by wavelet transform through the testing of numerous sorts of wavelets, we will extract the Mel scale cepstral coefficients (MFCC) of the transformed signals, and at the end classification study by vector machine (SVM) which is one of the algorithm of machine learning [20]

Summary

Introduction

The history of Parkinson's disease began in 1817 by James Parkinson [1]. It results in a slow and gradual destruction of neurons of the brain's dark substance. It is the second most common neurodegenerative disease, behind Alzheimer's disease. The acoustic treatment has been used recently in the diagnosis of many diseases. Always at the acoustic treatment we found Nawel SOUISSI and Adnane CHERIF they work on voice disorders identification [7]

Methods

Results

Conclusion