| Institution: | 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 |
| 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. |
