Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square

Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square

This paper describes the classification of infant cry with asphyxia using integration of Orthogonal Least Square and Support Vector Machine with Radial Basis Function kernel (OLS-SVM) and integration of Orthogonal Least Square with Multilayer Perceptron (OLS-MLP). The information embedded in the cry...

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Published in:Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012
Main Author: Sahak R.; Mansor W.; Khuan L.Y.; Zabidi A.; Yassin A.I.M.
Format: Conference paper
Language:English
Published: 2012
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864249983&doi=10.1109%2fBHI.2012.6211734&partnerID=40&md5=50618ac3b00c1b92a14199083d826e7a
id 2-s2.0-84864249983
spelling 2-s2.0-84864249983
Sahak R.; Mansor W.; Khuan L.Y.; Zabidi A.; Yassin A.I.M.
Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
2012
Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012


10.1109/BHI.2012.6211734
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864249983&doi=10.1109%2fBHI.2012.6211734&partnerID=40&md5=50618ac3b00c1b92a14199083d826e7a
This paper describes the classification of infant cry with asphyxia using integration of Orthogonal Least Square and Support Vector Machine with Radial Basis Function kernel (OLS-SVM) and integration of Orthogonal Least Square with Multilayer Perceptron (OLS-MLP). The information embedded in the cry signal was extracted using Mel Frequency Cepstrum Coefficient (MFCC) analysis. The extracted features were then selected according to its error reduction ratio (ERR) using OLS. MLP and SVM were then used to distinguish between asphyxiated infant cry and normal cry. Classification accuracy was computed to evaluate the performance of both methods. The OLS-SVM has produced high classification accuracy (94.34%) compared to OLS-MLP when C and γ were set to 1 and 0.013 respectively, and the selection of coefficients is 30% of 33 filter banks. © 2012 IEEE.


English
Conference paper
author Sahak R.; Mansor W.; Khuan L.Y.; Zabidi A.; Yassin A.I.M.
spellingShingle Sahak R.; Mansor W.; Khuan L.Y.; Zabidi A.; Yassin A.I.M.
Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
author_facet Sahak R.; Mansor W.; Khuan L.Y.; Zabidi A.; Yassin A.I.M.
author_sort Sahak R.; Mansor W.; Khuan L.Y.; Zabidi A.; Yassin A.I.M.
title Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
title_short Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
title_full Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
title_fullStr Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
title_full_unstemmed Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
title_sort Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square
publishDate 2012
container_title Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012
container_volume
container_issue
doi_str_mv 10.1109/BHI.2012.6211734
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864249983&doi=10.1109%2fBHI.2012.6211734&partnerID=40&md5=50618ac3b00c1b92a14199083d826e7a
description This paper describes the classification of infant cry with asphyxia using integration of Orthogonal Least Square and Support Vector Machine with Radial Basis Function kernel (OLS-SVM) and integration of Orthogonal Least Square with Multilayer Perceptron (OLS-MLP). The information embedded in the cry signal was extracted using Mel Frequency Cepstrum Coefficient (MFCC) analysis. The extracted features were then selected according to its error reduction ratio (ERR) using OLS. MLP and SVM were then used to distinguish between asphyxiated infant cry and normal cry. Classification accuracy was computed to evaluate the performance of both methods. The OLS-SVM has produced high classification accuracy (94.34%) compared to OLS-MLP when C and γ were set to 1 and 0.013 respectively, and the selection of coefficients is 30% of 33 filter banks. © 2012 IEEE.
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language English
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