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

• Main Authors: Safara, Fatemeh, C. Doraisamy, Shyamala, Azman, Azreen, Jantan, Azrul, Abdullah Ramaiah, Asri Ranga
• Format: Article
• Language: English; English
• Published: Elsevier 2013
• Online Access: http://psasir.upm.edu.my/id/eprint/30560/1/Multi-level%20basis%20selection%20of%20wavelet%20packet%20decomposition%20tree%20for%20heart%20sound%20classification..pdf; http://psasir.upm.edu.my/id/eprint/30560/

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.