A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests

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 Multi...

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Main Authors: Mahnaz Behroozi, Ashkan Sami
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:International Journal of Telemedicine and Applications
Online Access:http://dx.doi.org/10.1155/2016/6837498
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author Mahnaz Behroozi
Ashkan Sami
author_facet Mahnaz Behroozi
Ashkan Sami
author_sort Mahnaz Behroozi
collection DOAJ
description 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%.
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spelling doaj-art-5ab7e42ee17f49b3aaaf178b2d7cc2fb2025-02-03T01:04:45ZengWileyInternational Journal of Telemedicine and Applications1687-64151687-64232016-01-01201610.1155/2016/68374986837498A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal TestsMahnaz Behroozi0Ashkan Sami1Department of CSE and IT, School of Electrical Engineering and Computer Science, Shiraz University, Shiraz 71348-51154, IranDepartment of CSE and IT, School of Electrical Engineering and Computer Science, Shiraz University, Shiraz 71348-51154, IranRecently, 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%.http://dx.doi.org/10.1155/2016/6837498
spellingShingle Mahnaz Behroozi
Ashkan Sami
A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
International Journal of Telemedicine and Applications
title A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
title_full A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
title_fullStr A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
title_full_unstemmed A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
title_short A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
title_sort multiple classifier framework for parkinson s disease detection based on various vocal tests
url http://dx.doi.org/10.1155/2016/6837498
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