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A hybrid expert system approach for telemonitoring of vocal fold pathology

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Bibliographic Details
Main Authors: Hariharan, Muthusamy, Dr., Kemal, Polatb, Sindhu, Ravindran, Sazali, Yaacob, Prof. Dr.
Other Authors: hari@unimap.edu.my
Format: Article
Language:English
Published: Elsevier B.V. 2014
Subjects:
Classification
Compressed voice samples
Feature extraction
Feature weighting
Vocal fold pathology
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33162
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spelling my.unimap-331622014-03-27T06:47:45Z A hybrid expert system approach for telemonitoring of vocal fold pathology Hariharan, Muthusamy, Dr. Kemal, Polatb Sindhu, Ravindran Sazali, Yaacob, Prof. Dr. hari@unimap.edu.my s.yaacob@unimap.edu.my Classification Compressed voice samples Feature extraction Feature weighting Vocal fold pathology Link to publisher's homepage at https://www.elsevier.com/ Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology. 2014-03-27T06:47:45Z 2014-03-27T06:47:45Z 2013 Article Applied Soft Computing Journal, vol. 13(10), 2013, pages 4148-4161 1568-4946 http://www.sciencedirect.com/science/article/pii/S1568494613001932?via=ihub http://dspace.unimap.edu.my:80/dspace/handle/123456789/33162 en Elsevier B.V.
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Classification
Compressed voice samples
Feature extraction
Feature weighting
Vocal fold pathology
spellingShingle Classification
Compressed voice samples
Feature extraction
Feature weighting
Vocal fold pathology
Hariharan, Muthusamy, Dr.
Kemal, Polatb
Sindhu, Ravindran
Sazali, Yaacob, Prof. Dr.
A hybrid expert system approach for telemonitoring of vocal fold pathology
description Link to publisher's homepage at https://www.elsevier.com/
author2 hari@unimap.edu.my
author_facet hari@unimap.edu.my
Hariharan, Muthusamy, Dr.
Kemal, Polatb
Sindhu, Ravindran
Sazali, Yaacob, Prof. Dr.
format Article
author Hariharan, Muthusamy, Dr.
Kemal, Polatb
Sindhu, Ravindran
Sazali, Yaacob, Prof. Dr.
author_sort Hariharan, Muthusamy, Dr.
title A hybrid expert system approach for telemonitoring of vocal fold pathology
title_short A hybrid expert system approach for telemonitoring of vocal fold pathology
title_full A hybrid expert system approach for telemonitoring of vocal fold pathology
title_fullStr A hybrid expert system approach for telemonitoring of vocal fold pathology
title_full_unstemmed A hybrid expert system approach for telemonitoring of vocal fold pathology
title_sort hybrid expert system approach for telemonitoring of vocal fold pathology
publisher Elsevier B.V.
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33162
_version_ 1643797086069587968
score 13.209306

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