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Respiratory sound classification using cepstral features and support vector machine

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Bibliographic Details
Main Authors: Palaniappan, Rajkumar, Sundaraj, Kenneth, Prof. Dr.
Other Authors: prkmect@gmail.com
Format: Working Paper
Language:English
Published: IEEE Conference Publications 2014
Subjects:
Respiratory sound
MFCC
Support vector machine
Confusion matrix
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33429
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spelling my.unimap-334292014-04-05T10:09:56Z Respiratory sound classification using cepstral features and support vector machine Palaniappan, Rajkumar Sundaraj, Kenneth, Prof. Dr. prkmect@gmail.com kenneth@unimap.edu.my Respiratory sound MFCC Support vector machine Confusion matrix Link to publisher's homepage at http://ieeexplore.ieee.org/ Respiratory sound analysis provides vital information of the present condition of the Lungs. It can be used to assist medical professionals in differential diagnosis. In this paper, we intend to distinguish between normal (without any pathological condition), airway obstruction pathology and parenchymal pathology using respiratory sound recordings taken from RALE database. The proposed method uses Mel-frequency cepstral coefficients (MFCC) as features extracted from respiratory sounds. The extracted features are distinguished using support vector machine classifier (SVM). The classifier performance is analysed by using confusion matrix technique. A mean classification accuracy of 90.77% was reported using the proposed method. The performance analysis of the SVM classifier using confusion matrix revealed that normal, airway obstruction and parenchymal pathology are classified at 94.11%, 92.31% and 88.00% classification accuracy respectively. The analysis reveals that the proposed method shows promising outcome in distinguishing between the normal, airway obstruction and parenchymal pathology. 2014-04-05T10:09:56Z 2014-04-05T10:09:56Z 2013 Working Paper IEEE Recent Advances in Intelligent Computational Systems, 2013, pages 132-136 http://dspace.unimap.edu.my:80/dspace/handle/123456789/33429 en IEEE Conference Publications
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 Respiratory sound
MFCC
Support vector machine
Confusion matrix
spellingShingle Respiratory sound
MFCC
Support vector machine
Confusion matrix
Palaniappan, Rajkumar
Sundaraj, Kenneth, Prof. Dr.
Respiratory sound classification using cepstral features and support vector machine
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 prkmect@gmail.com
author_facet prkmect@gmail.com
Palaniappan, Rajkumar
Sundaraj, Kenneth, Prof. Dr.
format Working Paper
author Palaniappan, Rajkumar
Sundaraj, Kenneth, Prof. Dr.
author_sort Palaniappan, Rajkumar
title Respiratory sound classification using cepstral features and support vector machine
title_short Respiratory sound classification using cepstral features and support vector machine
title_full Respiratory sound classification using cepstral features and support vector machine
title_fullStr Respiratory sound classification using cepstral features and support vector machine
title_full_unstemmed Respiratory sound classification using cepstral features and support vector machine
title_sort respiratory sound classification using cepstral features and support vector machine
publisher IEEE Conference Publications
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33429
_version_ 1643797185369735168
score 13.222552

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