Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

被引：116

作者：

Safara, Fatemeh ^{[1
,2
]}

Doraisamy, Shyamala ^{[1
]}

Azman, Azreen ^{[1
]}

Jantan, Azrul ^{[1
]}

Ramaiah, Asri Ranga Abdullah ^{[3
]}

机构：

[1] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Dept Multimedia, Upm Serdang 43400, Selangor Darul, Malaysia

[2] Islamic Azad Univ, Islamshahr Branch, Dept Comp Engn, Tehran, Iran

[3] Serdang Hosp, Dept Cardiol, Kajang 43000, Selangor Darul, Malaysia

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2013年 / 43卷 / 10期

关键词：

Phonocardiographic signal (PCG); Heart murmur; Wavelet packet transform; Multi-level basis selection; Feature extraction; Relative energy; Support vector machine; MURMUR CLASSIFICATION; FEATURE-EXTRACTION; DISORDERS; IDENTIFICATION; SIGNALS; PCG;

D O I：

10.1016/j.compbiomed.2013.06.016

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. (c) 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

引用

页码：1407 / 1414

页数：8

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