title: Multi-level basis selection of wavelet packet decomposition tree for heart sound classification.
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.
authors.affiliation: Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia; Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Tehran, Iran. Electronic address: fsafara@yahoo.com.
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Safara
authors.forename: Shyamala
authors.initials: S
authors.lastname: Doraisamy
authors.forename: Azreen
authors.initials: A
authors.lastname: Azman
authors.forename: Azrul
authors.initials: A
authors.lastname: Jantan
authors.forename: Asri Ranga
authors.initials: AR
authors.lastname: Abdullah Ramaiah
mesh_terms: D006347:Heart Sounds / Q000502:physiology*; D006349:Heart Valve Diseases / Q000503:physiopathology; D006801:Humans; D010701:Phonocardiography / Q000145:classification*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D060388:Support Vector Machine
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