Multi-level basis selection of wavelet packet decomposition tree for heart sound classification |
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| Authors: | Fatemeh Safara Shyamala Doraisamy Azreen Azman Azrul Jantan Asri Ranga Abdullah Ramaiah |
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| Affiliation: | 1. Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia;2. Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Tehran, Iran;3. Cardiology Department, Serdang Hospital, Serdang, Jalan Puchong, 43000 Kajang, Selangor Darul Ehsan, Malaysia |
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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. |
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| Keywords: | AR, aortic regurgitation AS, aortic stenosis BBS, best basis selection DFT, discrete wavelet transform DWT, discrete wavelet transform ECG, electrocardiographic signal ENG, energy ET, energy threshold LDB, local discriminant basis MLBS, multi-level basis selection MR, mitral regurgitation PCG, phonocardiographic signal RBF, radial basis function RENG, relative energy SLBS, single-level basis selection SNR, signal -to-noise ratio STFT, short time fourier transform SVM, support vector machine WPT, wavelet packet transform |
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