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

被引：0
作者：
Belhoussine Drissi, Taoufiq [1 ]
Zayrit, Soumaya [1 ]
Nsiri, Benayad [2 ]
Ammoummou, Abdelkrim [1 ]
机构：
[1] Univ Hassan 2, Fac Sci Ain Chok, Lab Ind Engn Informat Proc & Logist GITIL, Casablanca, Morocco
[2] Mohammed V Univ Rabat, Res Ctr STIS, M2CS, Higher Sch Tech Educ Rabat ENSET, Rabat, Morocco
来源：
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2019年 / 10卷 / 03期
关键词：
Parkinson disease; discrete wavelet transform; MFCC; Support Vector Machine (SVM);
D O I：
10.14569/IJACSA.2019.0100315
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：125 / 132
页数：8
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