Multi-level basis selection of wavelet packet decomposition tree for heart sound classification.
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2013
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| 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/ |
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| Summary: | 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. |
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