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dc.contributor.authorPalaniappan, Rajkumar
dc.contributor.authorSundaraj, Kenneth, Prof. Dr.
dc.date.accessioned2014-04-05T10:09:56Z
dc.date.available2014-04-05T10:09:56Z
dc.date.issued2013
dc.identifier.citationIEEE Recent Advances in Intelligent Computational Systems, 2013, pages 132-136en_US
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33429
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractRespiratory 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.en_US
dc.language.isoenen_US
dc.publisherIEEE Conference Publicationsen_US
dc.subjectRespiratory sounden_US
dc.subjectMFCCen_US
dc.subjectSupport vector machineen_US
dc.subjectConfusion matrixen_US
dc.titleRespiratory sound classification using cepstral features and support vector machineen_US
dc.typeWorking Paperen_US
dc.contributor.urlprkmect@gmail.comen_US
dc.contributor.urlkenneth@unimap.edu.myen_US


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