Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Authors:
Fatemeh Safara;Shyamala Doraisamy;Azreen Azman;Azrul Jantan;Asri Ranga Abdullah Ramaiah
Affiliations:
-;-;-;-;-
Venue:
Computers in Biology and Medicine
Year:
2013

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Abstract

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.