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. 1. Introduction Accurate and early diagnosis of cardiac diseases is of great importance which is possible through heart auscultation. It is the most common and widely recommended method to screen for structural abnormalities of the cardiovascular system. Detecting relevant characteristics and forming a diagnosis based on the sounds heard through a stethoscope, however, is a skill that can take years to be acquired and refine. The efficiency and accuracy of diagnosis based on heart sound auscultation can be improved considerably by using digital signal processing techniques to analyze phonocardiographic (PCG) signals [1–3]. Phonocardiography is the recording of sonic vibrations of heart and blood circulation. PCG signals can provide valuable information regarding the performance of heart valves, therefore it has a high potential for detecting various heart diseases [4, 5]. Two loudest heart sounds are the first and the second sounds, referred to as S1 and S2. The time interval between S1 and S2 is called systole and the time interval between S2 and next S1 is called diastole. Normal heart sounds are low-frequency transient signals produced by the heart valves while pathological heart sounds, such as heart murmurs, are high-frequency, noise-like sounds [6]. Heart murmurs are produced as a result of turbulence in blood flow through narrow cardiac valves or reflow through the atrioventricular valves. Congenital heart defects or acquired heart valve diseases are often
References
[1]
Y. Chen, S. Wang, C.-H. Shen, and F. Choy, “Intelligent identification of childhood musical murmurs,” Journal of Healthcare Engineering, vol. 3, pp. 125–140, 2012.
[2]
L. H. Cherif, S. M. Debbal, and F. Bereksi-Reguig, “Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform,” Expert Systems with Applications, vol. 37, no. 2, pp. 913–918, 2010.
[3]
S. Choi, Y. Shin, and H. K. Park, “Selection of wavelet packet measures for insufficiency murmur identification,” Expert Systems with Applications, vol. 38, no. 4, pp. 4264–4271, 2011.
[4]
C. N. Gupta, R. Palaniappan, S. Swaminathan, and S. M. Krishnan, “Neural network classification of homomorphic segmented heart sounds,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 286–297, 2007.
[5]
L. Jia, D. Song, L. Tao, and Y. Lu, “Heart sounds classification with a fuzzy neural network method with structure learning,” in Advances in Neural Networks, vol. 7368 of Lecture Notes in Computer Science, pp. 130–140, 2012.
[6]
B. Ergen, Y. Tatar, and H. O. Gulcur, “Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 15, pp. 371–381, 2011.
[7]
W. C. Kao and C. C. Wei, “Automatic phonocardiograph signal analysis for detecting heart valve disorders,” Expert Systems with Applications, vol. 38, no. 6, pp. 6458–6468, 2011.
[8]
M. Salama, A. Hassanien, J. Platos, A. Fahmy, and V. Snasel, “Rough sets-based identification of heart valve diseases using heart sounds,” in Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems, pp. 667–676, 2012.
[9]
S. Sanei, M. Ghodsi, and H. Hassani, “An adaptive singular spectrum analysis approach to murmur detection from heart sounds,” Medical Engineering & Physics, vol. 33, no. 3, pp. 362–367, 2011.
[10]
Y. L. Tseng, P. Y. Ko, and F. S. Jaw, “Detection of the third and fourth heart sounds using Hilbert-Huang transform,” BioMedical Engineering OnLine, vol. 11, article 8, 2012.
[11]
S. Yuenyong, A. Nishihara, W. Kongprawechnon, and K. Tungpimolrut, “A framework for automatic heart sound analysis without segmentation,” BioMedical Engineering Online, vol. 10, article 13, 2011.
[12]
C. Ahlstrom, P. Hult, P. Rask et al., “Feature extraction for systolic heart murmur classification,” Annals of Biomedical Engineering, vol. 34, no. 11, pp. 1666–1677, 2006.
[13]
S. Choi and Z. Jiang, “Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique,” Computers in Biology and Medicine, vol. 40, no. 1, pp. 8–20, 2010.
[14]
S. Choi, “Detection of valvular heart disorders using wavelet packet decomposition and support vector machine,” Expert Systems with Applications, vol. 35, no. 4, pp. 1679–1687, 2008.
[15]
F. Safara, S. Doraisamy, A. Azman, and A. Jantan, “Heart sounds clustering using a combination of temporal, spectral and geometric 8 features,” Computing in Cardiology, vol. 39, pp. 217–220, 2012.
[16]
S. Vitulano and A. Casanova, “The role of entropy: mammogram analysis,” Image Analysis and Recognition, vol. 5112, pp. 863–872, 2008.
[17]
C. Ahlstrom, K. H？glund, P. Hult, J. H？ggstr？m, C. Kvart, and P. Ask, “Distinguishing innocent murmurs from murmurs caused by aortic stenosis by recurrence quantification analysis,” in Proceedings of the Enformatika Transactions on Engineering, Computing and Technology, pp. 1305–5313, December 2006.
[18]
S. Babaei and A. Geranmayeh, “Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals,” Computers in Biology and Medicine, vol. 39, no. 1, pp. 8–15, 2009.
[19]
Y. Chen, S. Wang, C.-H. Shen, and F. K. Choy, “Matrix decomposition based feature extraction for murmur classification,” Medical Engineering & Physics, vol. 34, no. 6, pp. 756–761, 2012.
[20]
S. M. Debbal and F. Bereksi-Reguig, “Time-frequency analysis of the first and the second heartbeat sounds,” Applied Mathematics and Computation, vol. 184, no. 2, pp. 1041–1052, 2007.
[21]
M. R. Homaeinezhad, S. A. Atyabi, E. Deneshvar, A. Ghaffari, and M. Tahmasebi, “Optimal delineation of PCG sounds via false-alarm bounded segmentation of a wavelet-based principal components analyzed metric,” International Journal for Numerical Methods in Biomedical Engineering, vol. 27, pp. 1711–1739, 2011.
[22]
A. Sengur and I. Turkoglu, “A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases,” Expert Systems with Applications, vol. 35, no. 3, pp. 1011–1020, 2008.
