Voice assessments for detecting patients with neurological diseases using PCA and NPCA

Voice assessments for detecting patients with neurological diseases using PCA and NPCA In this study, we wanted to discriminate between 30 patients who suffer from Parkinson’s disease (PD) and 20 patients with other neurological diseases (ND). All participants were asked to pronounce sustained vowel /a/ hold as long as possible at comfortable level. The analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used principal component analysis (PCA) and nonlinear PCA (NPCA). These techniques reduce the number of parameters and select the most effective ones to be used for classification. Support vector machine and k-nearest neighbor with different kernels was used for classification. We obtained accuracy up to 88% for discrimination between PD patients ND patients using KNN with k equal to three and five. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Speech Technology Springer Journals

Voice assessments for detecting patients with neurological diseases using PCA and NPCA

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Signal,Image and Speech Processing; Social Sciences, general; Artificial Intelligence (incl. Robotics)
ISSN
1381-2416
eISSN
1572-8110
D.O.I.
10.1007/s10772-017-9438-9
Publisher site
See Article on Publisher Site

Abstract

In this study, we wanted to discriminate between 30 patients who suffer from Parkinson’s disease (PD) and 20 patients with other neurological diseases (ND). All participants were asked to pronounce sustained vowel /a/ hold as long as possible at comfortable level. The analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used principal component analysis (PCA) and nonlinear PCA (NPCA). These techniques reduce the number of parameters and select the most effective ones to be used for classification. Support vector machine and k-nearest neighbor with different kernels was used for classification. We obtained accuracy up to 88% for discrimination between PD patients ND patients using KNN with k equal to three and five.

Journal

International Journal of Speech TechnologySpringer Journals

Published: Jul 8, 2017

References

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