Neural classification of lung sounds using wavelet coefficients
Neural classification of lung sounds using wavelet coefficients
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DOI：
10.1016/s0010-4825(03)00092-1
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发表时间：
2004-09-01
影响因子：
7.7
通讯作者：
Malmurugan, N
中科院分区：
文献类型：
--
作者：
Kandaswamy, A;Kumar, CS;Malmurugan, N
电子听诊是利用肺音评估呼吸系统状况的有效技术。由于肺音信号是非平稳的，传统的频率分析方法在诊断分类方面并不十分成功。本文讨论了一种使用小波变换分析肺音信号并使用人工神经网络 (ANN) 进行分类的新方法。使用小波变换将肺声音信号分解为频率子带，并从子带中提取一组统计特征来表示小波系数的分布。采用基于 ANN 的系统，使用弹性反向传播算法进行训练，将肺音分类为六类之一：正常、喘息、爆裂声、嘎嘎声、喘鸣或干咳声。 (C) 2004 Elsevier Ltd. 保留所有权利。
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.