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A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
Joint Authors
Source
International Journal of Telemedicine and Applications
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers.
For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP).
Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD.
However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms.
Thus, summarizing the vocal tests may lead into loss of valuable information.
In order to address this issue, the classification setting must take what has been said into account.
As a solution, we introduced a new framework that applies an independent classifier for each vocal test.
The final classification result would be a majority vote from all of the classifiers.
When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.
American Psychological Association (APA)
Behroozi, Mahnaz& Sami, Ashkan. 2016. A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests. International Journal of Telemedicine and Applications،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1107092
Modern Language Association (MLA)
Behroozi, Mahnaz& Sami, Ashkan. A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests. International Journal of Telemedicine and Applications No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1107092
American Medical Association (AMA)
Behroozi, Mahnaz& Sami, Ashkan. A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests. International Journal of Telemedicine and Applications. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1107092
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1107092