Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine

Parkinson’s disease (PD) is a neurodegenerative disorder that causes severe motor and cognitive dysfunctions. Several types of physiological signals can be analyzed to accurately detect PD by using machine learning methods. This work considers the diagnosis of PD based on voice patterns. In particul...

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Bibliographic Details
Published inBiomedical signal processing and control Vol. 49; pp. 427 - 433
Main Authors Lahmiri, Salim, Shmuel, Amir
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2019
Subjects
Bayesian optimization
Classification
Features ranking
Parkinson’s disease
Radial basis function
Support vector machine
Voice disorder
Radial basis function
Voice disorder
Classification
Support vector machine
Bayesian optimization
Features ranking
Parkinson’s disease
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Summary:Parkinson’s disease (PD) is a neurodegenerative disorder that causes severe motor and cognitive dysfunctions. Several types of physiological signals can be analyzed to accurately detect PD by using machine learning methods. This work considers the diagnosis of PD based on voice patterns. In particular, we focus on assessing the performance of eight different pattern ranking techniques (also termed feature selection methods) when coupled with nonlinear support vector machine (SVM) to distinguish between PD patients and healthy control subjects. The parameters of the radial basis function kernel of the SVM classifier were optimized by using Bayesian optimization technique. Our results show that the receiver operating characteristic and the Wilcoxon-based ranking techniques provide the highest sensitivity and specificity.
ISSN:1746-8094
1746-8108
DOI:10.1016/j.bspc.2018.08.029