title: Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson's disease.
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.
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Yang
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Zheng
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Luo
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Suxian
authors.initials: S
authors.lastname: Cai
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Wu
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Kaizhi
authors.initials: K
authors.lastname: Liu
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: Meihong
authors.initials: M
authors.lastname: Wu
authors.affiliation: Department of Rehabilitation, Zhongshan Hospital Xiamen University, Xiamen, Fujian, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada.
authors.forename: Sridhar
authors.initials: S
authors.lastname: Krishnan
mesh_terms: D000368:Aged; D019540:Area Under Curve; D016002:Discriminant Analysis; D055154:Dysphonia / Q000175:diagnosis* / Q000209:etiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000150:complications / Q000473:pathology*; D010699:Phonation / Q000502:physiology*; D025341:Principal Component Analysis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D060388:Support Vector Machine