By Topic

Energy-based feature ranking for assessing the dysphonia measurements in Parkinson detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ji, W. ; Coll. of Commun. & Inf. Eng., Nanjing Univ. of Posts & Telecommun., Nanjing, China ; Li, Y.

The Parkinson's disease (PD) detection based on dysphonia has been drawn significant attention. However, all dysphonia measurements differ in the uncontrolled acoustic environments. In order to gain as much reliability as possible, measurements should be assessed and the robust ones are chosen. In this study, motivated by statistical learning theory, the problem of PD detection is addressed to classify the participant as healthy or PD using support vector machine (SVM) with the dysphonia measurements as the input feature vector. Therefore an energy-based feature-ranking algorithm is adopted to assess the dysphonia measurements. Moreover, in order to improve the stability of the proposed algorithm, an ensemble version is also presented where multiple feature-ranking results are aggregated. The experimental results on PD data sets have shown the proposed algorithm outperforms other classic ones, and the ensemble version obtain the higher stability than single one.

Published in:

Signal Processing, IET  (Volume:6 ,  Issue: 4 )