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Advances in Bioinformatics
Volume 2012, Article ID 327269, 6 pages
Research Article

Wavelet Packet Entropy for Heart Murmurs Classification

1Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Islamshahr, Tehran 3314767653, Iran
2Faculty of Computer Science and Information Technology, 43400 Serdang, Selangor Darul Ehsan, Malaysia
3Department of Cardiology, Serdang Hospital, 43000 Kajang, Selangor Darul Ehsan, Malaysia

Received 31 July 2012; Revised 5 October 2012; Accepted 24 October 2012

Academic Editor: Tatsuya Akutsu

Copyright © 2012 Fatemeh Safara et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.