计算机工程与应用2017，Vol.53Issue(22)：116-120,149,6.DOI:10.3778/j.issn.1002-8331.1605-0330
基于LDA和小波分解的肺音特征提取方法
Feature extraction method of lung sound based on LDA and wavelet decom- position
摘要
Abstract
The feature vectors, which are extracted from lung sounds with the wavelet decomposition, have higher dimen-sion. To solve the problem, a method to extract the feature of lung sounds is proposed, which combines the linear discrimi-nant analysis and the wavelet decomposition. In the method, the lung sounds are firstly executed the wavelet decomposi-tion. Then the wavelet coefficients from the wavelet decomposition are transformed into the wavelet energy feature vec-tors. Next the dimension of the feature vectors is reduced with the linear discriminant analysis. Finally, the lung sounds are recognized with SVM according to the low dimensional feature vector. In experiments, three kinds of lung sound sig-nals are used:normal, crackle and wheeze. The wavelet energy feature vectors with 8 dimension are reduced to 2 dimen-sion with the presented method. The lung sounds are classified with SVM according to the two-dimensional feature vec-tors. The results are compared with the results from the original data. The results demonstrate that the recognition accuracy is greatly improved through reducing the dimension of the wavelet energy feature vector with the linear discriminant anal-ysis. At the same time, the proposed method is compared with other lung sound feature extraction method. The results also show that the recognition accuracy is higher by combining the linear discriminant analysis and the wavelet decomposition.关键词
肺音/线性判别分析/小波分解/支持向量机(SVM)Key words
lung sound/linear discriminant analysis/wavelet decomposition/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
石陆魁,刘文浩,李站茹..基于LDA和小波分解的肺音特征提取方法[J].计算机工程与应用,2017,53(22):116-120,149,6.基金项目
天津市应用基础与前沿技术研究计划重点项目(No.14JCZDJC31600) （No.14JCZDJC31600）
河北省自然科学基金专项(No.F2016202144) （No.F2016202144）
河北省高等学校科学技术研究重点项目支持(No.ZD2014030). （No.ZD2014030）
