Neural classification of lung sounds using wavelet coefficients

被引：260

作者：

Kandaswamy, A

Kumar, CS ^{[1
]}

Ramanathan, RP

Jayaraman, S

Malmurugan, N

机构：

[1] PSG Coll Technol, Dept Elect & Commun Engn, Coimbatore 641004, Tamil Nadu, India

[2] PSG Coll Technol, Dept Elect & Elect Engn, Coimbatore 641004, Tamil Nadu, India

[3] PSG Inst Med Sci & Res, Dept Pulmonol, Coimbatore 641004, Tamil Nadu, India

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2004年 / 34卷 / 06期

关键词：

respiratory system diagnosis; auscultation; lung sound analysis; discrete wavelet transform; artificial neural network;

D O I：

10.1016/S0010-4825(03)00092-1

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient backpropagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：523 / 537

页数：15

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