Daubechies Wavelet Cepstral Coefficients for Parkinson's Disease Detection

被引：2

作者：

Zayrit, Soumaya ^{[1
]}

Belhoussine Drissi, Taoufiq ^{[1
]}

Ammoumou, Abdelkrim ^{[1
]}

Nsiri, Benayad ^{[2
]}

机构：

[1] Univ Hassan 2, Fac Sci Ain Chok, Lab Ind Engn Informat Proc & Logist, Casablanca, Morocco

[2] M2CS Mohammed V Univ Rabat, Higher Sch Tech Educ Rabat ENSET, Res Ctr STIS, Rabat, Morocco

来源：

COMPLEX SYSTEMS | 2020年 / 29卷 / 03期

关键词：

Parkinson's disease; Daubechies wavelet; MFCC; SVM; DIAGNOSIS; SPEECH;

D O I：

10.25088/ComplexSystems.29.3.729

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The aim of this paper is to evaluate the performance of the approach that focuses on support vector machine (SVM) classification of vocal recording to differentiate between patients affected by Parkinson's disease (PD) and healthy patients. Our study was based on the condition of 38 patients, some of whom are healthy and others who suffer from PD. The study was carried out as follows: The extraction of cepstral coefficients was reached through the transformation of the speech signal by discrete wavelet transform (DWT) and also through cepstral analysis by using the mel scale. At the end, a classification was done by the use of the two kernels linear and radial basis function (RBF) of the SVM classifier.

引用

页码：729 / 739

页数：11

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