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中文
Yunfeng Wu

Associate professor

Supervisor of Master's Candidates


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Date of Employment:2009-12-14

School/Department:信息学院

Education Level:博士研究生毕业

Business Address:厦门大学翔安校区西部片区6号楼(睿信楼)202室

Gender:Male

Contact Information:yunfengwu@xmu.edu.cn

Degree:Doctor of Engineering (D.Eng.)

Status:在职

Alma Mater:北京邮电大学

Discipline:生物医学工程
信号与信息处理

Academic Honor:

2013   Outstanding talents in the new century

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Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson's disease

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DOI number:10.1371/journal.pone.0088825

Journal:PLOS ONE

Abstract:Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson's disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher's linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified.

Co-author:Fang Zheng,Xin Luo,Suxian Cai,Kaizhi Liu,Meihong Wu,Jian Chen,Sridhar Krishnan

First Author:Shanshan Yang

Indexed by:Article

Correspondence Author:Yunfeng Wu*

Volume:9

Issue:2

Page Number:e88825

Translation or Not:no

Date of Publication:2014-02-20

Included Journals:SCI

Links to published journals:https://doi.org/10.1371/journal.pone.0088825