Commenced in January 2007
Paper Count: 31168
Wavelet Feature Selection Approach for Heart Murmur Classification
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1108094Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2027
 Roy, D. L. (2003). The paediatrician and cardiac auscultation. Paediatrics& child health, 8(9), 561.
 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.
 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.
 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.
 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).
 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.
 Rangayyan, R. M., &Lehner, R. J. (1986). Phonocardiogram signal analysis: a review. Critical reviews in biomedical engineering, 15(3), 211-236.
 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.
 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.
 Guyon, I., &Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157-1182.
 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).
 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.
 Singh, M., & Cheema, A. (2013). Heart Sounds Classification using Feature Extraction of Phonocardiography Signal. International Journal of Computer Applications, 77(4).
 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.
 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).
 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).
 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.
 Kaur, E. R., & Singh, J. Comparative Study Of DWT Based Image Compression Using Haar, Daub & Coif Wavelets.
 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.
 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.
 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.
 Kennedy, J. (2010). Particle swarm optimization. In Encyclopedia of Machine Learning (pp. 760-766). Springer US.
 Ding, S., & Chen, L. (2010). Intelligent Optimization Methods for High- Dimensional Data Classification for Support Vector Machines. Intelligent Information Management, 2, 354.
 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.
 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.
 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.
 Kataria, A., & Singh, M. D. A Review of Data Classification Using KNearest Neighbour Algorithm.
 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.
 Srivastava, D. K., & Bhambhu, L. (2010). Data classification using support vector machine.