用人工神经网络对呼吸音进行分类

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Pub Date : 2001-10-28 DOI:10.1109/IEMBS.2001.1019035

Z. Dokur, T. Ölmez

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引用次数: 35

摘要

本文介绍了一种哮喘患者与正常人呼吸音的分类方法。小波变换应用于包含256个样本的窗口。特征向量的元素由小波系数得到。采用动态规划方法选择最佳特征元素。使用生长与学习(GAL)神经网络进行分类。观察到，GAL网络对哮喘患者和健康受试者的RSs进行了成功的分类。

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Classification of respiratory sounds by using an artificial neural network

In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. A wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. A Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

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来源期刊

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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