| Literature DB >> 24034732 |
Fatemeh Safara1, Shyamala Doraisamy, Azreen Azman, Azrul Jantan, Asri Ranga Abdullah Ramaiah.
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.Entities:
Keywords: AR; AS; BBS; DFT; DWT; ECG; ENG; ET; Feature extraction; Heart murmur; LDB; MLBS; MR; Multi-level basis selection; PCG; Phonocardiographic signal (PCG); RBF; RENG; Relative energy; SLBS; SNR; STFT; SVM; Support vector machine; WPT; Wavelet packet transform; aortic regurgitation; aortic stenosis; best basis selection; discrete wavelet transform; electrocardiographic signal; energy; energy threshold; local discriminant basis; mitral regurgitation; multi-level basis selection; phonocardiographic signal; radial basis function; relative energy; short time fourier transform; signal -to-noise ratio; single-level basis selection; support vector machine; wavelet packet transform
Mesh：
Year: 2013 PMID： 24034732 DOI： 10.1016/j.compbiomed.2013.06.016
Source DB: PubMed Journal: Comput Biol Med ISSN： 0010-4825 Impact factor: 4.589