Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease
Identifieur interne : 001696 ( Ncbi/Curation ); précédent : 001695; suivant : 001697Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease
Auteurs : Shanshan Yang [République populaire de Chine] ; Fang Zheng [République populaire de Chine] ; Xin Luo [République populaire de Chine] ; Suxian Cai [République populaire de Chine] ; Yunfeng Wu [République populaire de Chine] ; Kaizhi Liu [République populaire de Chine] ; Meihong Wu [République populaire de Chine] ; Jian Chen [République populaire de Chine] ; Sridhar Krishnan [Canada]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
English descriptors
- KwdEn :
- Aged, Area Under Curve, Discriminant Analysis, Dysphonia (diagnosis), Dysphonia (etiology), Female, Humans, Male, Middle Aged, Parkinson Disease (complications), Parkinson Disease (pathology), Phonation (physiology), Principal Component Analysis, ROC Curve, Sensitivity and Specificity, Support Vector Machine.
- MESH :
- complications : Parkinson Disease.
- diagnosis : Dysphonia.
- etiology : Dysphonia.
- pathology : Parkinson Disease.
- physiology : Phonation.
- Aged, Area Under Curve, Discriminant Analysis, Female, Humans, Male, Middle Aged, Principal Component Analysis, ROC Curve, Sensitivity and Specificity, Support Vector Machine.
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
Url:
DOI: 10.1371/journal.pone.0088825
PubMed: 24586406
PubMed Central: 3930574
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Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Zheng, Fang" sort="Zheng, Fang" uniqKey="Zheng F" first="Fang" last="Zheng">Fang Zheng</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Luo, Xin" sort="Luo, Xin" uniqKey="Luo X" first="Xin" last="Luo">Xin Luo</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Cai, Suxian" sort="Cai, Suxian" uniqKey="Cai S" first="Suxian" last="Cai">Suxian Cai</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Wu, Yunfeng" sort="Wu, Yunfeng" uniqKey="Wu Y" first="Yunfeng" last="Wu">Yunfeng Wu</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Liu, Kaizhi" sort="Liu, Kaizhi" uniqKey="Liu K" first="Kaizhi" last="Liu">Kaizhi Liu</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Wu, Meihong" sort="Wu, Meihong" uniqKey="Wu M" first="Meihong" last="Wu">Meihong Wu</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Science and Technology, Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Chen, Jian" sort="Chen, Jian" uniqKey="Chen J" first="Jian" last="Chen">Jian Chen</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Rehabilitation, Zhongshan Hospital Xiamen University, Xiamen, Fujian, China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Rehabilitation, Zhongshan Hospital Xiamen University, Xiamen, Fujian</wicri:regionArea><wicri:noRegion>Fujian</wicri:noRegion></affiliation></author><author><name sortKey="Krishnan, Sridhar" sort="Krishnan, Sridhar" uniqKey="Krishnan S" first="Sridhar" last="Krishnan">Sridhar Krishnan</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Area Under Curve</term><term>Discriminant Analysis</term><term>Dysphonia (diagnosis)</term><term>Dysphonia (etiology)</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Parkinson Disease (complications)</term><term>Parkinson Disease (pathology)</term><term>Phonation (physiology)</term><term>Principal Component Analysis</term><term>ROC Curve</term><term>Sensitivity and Specificity</term><term>Support Vector Machine</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Dysphonia</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Dysphonia</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Phonation</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Area Under Curve</term><term>Discriminant Analysis</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Principal Component Analysis</term><term>ROC Curve</term><term>Sensitivity and Specificity</term><term>Support Vector Machine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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 <italic>a posteriori</italic> (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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Benninger, Ms" uniqKey="Benninger M">MS Benninger</name></author><author><name sortKey="Ahuja, As" uniqKey="Ahuja A">AS Ahuja</name></author><author><name sortKey="Gardner, G" uniqKey="Gardner G">G Gardner</name></author><author><name sortKey="Grywalski, C" uniqKey="Grywalski C">C Grywalski</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Behroozmand, R" uniqKey="Behroozmand R">R Behroozmand</name></author><author><name sortKey="Almasganj, F" uniqKey="Almasganj F">F Almasganj</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vaziri, G" uniqKey="Vaziri G">G Vaziri</name></author><author><name 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Wu</name></author><author><name sortKey="Cai, S" uniqKey="Cai S">S Cai</name></author><author><name sortKey="Yang, S" uniqKey="Yang S">S Yang</name></author><author><name sortKey="Zheng, F" uniqKey="Zheng F">F Zheng</name></author><author><name sortKey="Xiang, N" uniqKey="Xiang N">N Xiang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sakar, Co" uniqKey="Sakar C">CO Sakar</name></author><author><name sortKey="Kursun, O" uniqKey="Kursun O">O Kursun</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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