Dysphonic Voice Pattern Analysis of Patients in Parkinson’s Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods
Author:
Affiliation:
1. School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China
2. Department of Rehabilitation, Zhongshan Hospital, Xiamen University, 201 Hubin South Road, Xiamen, Fujian 361004, China
Abstract
Funder
Fujian Province University
Publisher
Hindawi Limited
Subject
Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine
Link
http://downloads.hindawi.com/journals/cmmm/2017/4201984.pdf
Reference32 articles.
1. Multiparametric Evaluation of Dysphonic Severity
2. Clinical Evaluation of Parkinson???s-Related Dysphonia
3. Perceptual evaluation of voice quality and its correlation with acoustic measurements
4. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection
5. Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease
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