%0 Journal Article
	%A G. Venkata Hari Prasad and  P. Rajesh Kumar
	%D 2015
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 99, 2015
	%T Wavelet Feature Selection Approach for Heart Murmur Classification
	%U https://publications.waset.org/pdf/10002123
	%V 99
	%X 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.
	%P 315 - 322