Wavelet Feature Selection Approach for Heart Murmur Classification

Phonocardiography is important in appraisal of congenital heart disease and pulmonary hypertension as it reflects the duration of right ventricular systoles. The systolic murmur in patients with intra-cardiac shunt decreases as pulmonary hypertension develops and may eventually disappear completely as the pulmonary pressure reaches systemic level. Phonocardiography and auscultation are non-invasive, low-cost, and accurate methods to assess heart disease. In this work an objective signal processing tool to extract information from phonocardiography signal using Wavelet is proposed to classify the murmur as normal or abnormal. Since the feature vector is large, a Binary Particle Swarm Optimization (PSO) with mutation for feature selection is proposed. The extracted features improve the classification accuracy and were tested across various classifiers including Naïve Bayes, kNN, C4.5, and SVM.

Authors:

• G. Venkata Hari Prasad
• P. Rajesh Kumar

Keywords:

• Phonocardiography
• Coiflet
• Feature selection
• Particle Swarm Optimization.

References:

[1] Roy, D. L. (2003). The paediatrician and cardiac auscultation.
Paediatrics& child health, 8(9), 561.
[2] Donnerstein, R. L., & Thomsen, V. S. (1994). Hemodynamic and
anatomic factors affecting the frequency content of Still's innocent
murmur. The American journal of cardiology, 74(5), 508-510.
[3] Barschdorff, D., Femmer, U., &Trowitzsch, E. (1995, September).
Automatic phonocardiogram signal analysis in infants based on wavelet
transforms and artificial neural networks. In Computers in Cardiology
1995 (pp. 753-756). IEEE.
[4] Shino, H., Yoshida, H., Yana, K., Harada, K., Sudoh, J., &Harasewa, E.
(1996). Detection and classification of systolic murmur for
phonocardiogram screening. In Engineering in Medicine and Biology
Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the
18th Annual International Conference of the IEEE (Vol. 1, pp. 123-124).
IEEE.
[5] Durand, L. G., &Pibarot, P. (1995). Digital signal processing of the
phonocardiogram: review of the most recent advancements. Critical
Reviews™ in Biomedical Engineering, 23(3-4).
[6] Bogdanović, V., Božić, I., Gavrovska, A., Stojić, V., &Jakovljević, V.
(2013). Phonocardiography-based mitral valve prolapse detection using
an artificial neural network. Serbian Journal of Experimental and
Clinical Research, 14(3), 113-120.
[7] Rangayyan, R. M., &Lehner, R. J. (1986). Phonocardiogram signal
analysis: a review. Critical reviews in biomedical engineering, 15(3),
211-236.
[8] Reynolds, D. A., & Rose, R. C. (1995). Robust text-independent speaker
identification using Gaussian mixture speaker models. Speech and
Audio Processing, IEEE Transactions on, 3(1), 72-83.
[9] Lung, S. Y. (2007). Efficient text independent speaker recognition with
wavelet feature selection based multilayered neural network using
supervised learning algorithm. Pattern Recognition, 40(12), 3616-3620.
[10] Guyon, I., &Elisseeff, A. (2003). An introduction to variable and feature
selection. The Journal of Machine Learning Research, 3, 1157-1182.
[11] D. B., Brennan, T., Zuhlke, L. J., Abdelrahman, H. Y., Ntusi, N.,
Clifford, G. D. & Tarassenko, L. (2014, May). Signal quality
classification of mobile phone-recorded phonocardiogram signals. IEEE
International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2014 (pp. 1335-1339).
[12] Gavrovska, A., Zajić, G., Reljin, I., & Reljin, B. (2013). Classification of
prolapsed mitral valve versus healthy heart from phonocardiograms by
multifractal analysis. Computational and mathematical methods in
medicine, 2013.
[13] Singh, M., & Cheema, A. (2013). Heart Sounds Classification using
Feature Extraction of Phonocardiography Signal. International Journal
of Computer Applications, 77(4).
[14] Sinha, Rakesh Kumar, et al. "K-Nearest Neighborhood Approach to
Identify Level of Left Ventricular Ejection Fraction From
Phonocardiogram." Journal of Clinical Engineering 38.2 (2013): 75-78.
[15] Ramos, J. P., Carvalho, P., & Coimbra, M. (2013, July). Towards a timefeature
independent phonocardiogram segmentation. Annual
International Conference of the IEEE in Engineering in Medicine and
Biology Society (EMBC), 2013 35th (pp. 2116-2119).
[16] Dixit, A., &Majumdar, S. (2013). Comparative Analysis Of Coiflet And
Daubechies Wavelets Using Global Threshold For Image De-Noising.
International Journal of Advances in Engineering & Technology,6(5).
[17] Kumar, V. S., & Reddy, M. I. S. (2012). Image Compression
Techniques by using Wavelet Transform. Journal of information
engineering and applications, 2(5), 35-39.
[18] Kaur, E. R., & Singh, J. Comparative Study Of DWT Based Image
Compression Using Haar, Daub & Coif Wavelets.
[19] Zhao, F., Liu, J., Liu, J., Guibas, L., & Reich, J. (2003). Collaborative
signal and information processing: an information-directed approach.
Proceedings of the IEEE, 91(8), 1199-1209.
[20] Weiss, L. E., Sanderson, A. C., & Neuman, C. P. (1987). Dynamic
sensor-based control of robots with visual feedback. Robotics and
Automation, IEEE Journal of, 3(5), 404-417.
[21] Sugumaran, V., V. Muralidharan, and K. I. Ramachandran. "Feature
selection using decision tree and classification through proximal support
vector machine for fault diagnostics of roller bearing." Mechanical
systems and signal processing 21.2 (2007): 930-942.
[22] Kennedy, J. (2010). Particle swarm optimization. In Encyclopedia of
Machine Learning (pp. 760-766). Springer US.
[23] Ding, S., & Chen, L. (2010). Intelligent Optimization Methods for High-
Dimensional Data Classification for Support Vector Machines.
Intelligent Information Management, 2, 354.
[24] Sharma, L., Pathak, B. K., & Sharma, N. (2012). Breaking of simplified
data encryption standard using binary particle swarm optimization.
IJCSI International Journal of Computer Science Issues, 9(3), 1694-
0814.
[25] Das, S., Abraham, A., & Konar, A. (2008). Particle swarm optimization
and differential evolution algorithms: technical analysis, applications and hybridization perspectives. In Advances of Computational
Intelligence in Industrial Systems (pp. 1-38). Springer Berlin
Heidelberg.
[26] Ting, S. L., Ip, W. H., & Tsang, A. H. (2011). Is Naive Bayes a good
classifier for document classification?. International Journal of Software
Engineering and Its Applications, 5(3), 37.
[27] Kataria, A., & Singh, M. D. A Review of Data Classification Using KNearest
Neighbour Algorithm.
[28] Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao, V. (2013).
Predicting Students' Performance Using ID3 and C4. 5 Classification
Algorithms. arXiv preprint arXiv:1310.2071.
[29] Srivastava, D. K., & Bhambhu, L. (2010). Data classification using
support vector machine.