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DOI: 10.14569/IJACSA.2019.0100315
PDF

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

Author 1: Taoufiq BELHOUSSINE DRISSI
Author 2: Soumaya ZAYRIT
Author 3: Benayad NSIRI
Author 4: Abdelkrim AMMOUMMOU

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.

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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.

Keywords: Parkinson disease; discrete wavelet transform; MFCC; Support Vector Machine (SVM)

Taoufiq BELHOUSSINE DRISSI, Soumaya ZAYRIT, Benayad NSIRI and Abdelkrim AMMOUMMOU, “Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100315

@article{DRISSI2019,
title = {Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100315},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100315},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Taoufiq BELHOUSSINE DRISSI and Soumaya ZAYRIT and Benayad NSIRI and Abdelkrim AMMOUMMOU}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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