关键词: 肺音, 线性判别分析, 小波分解, 支持向量机（SVM）

Abstract: The feature vectors, which are extracted from lung sounds with the wavelet decomposition, have higher dimension. To solve the problem, a method to extract the feature of lung sounds is proposed, which combines the linear discriminant analysis and the wavelet decomposition. In the method, the lung sounds are firstly executed the wavelet decomposition. Then the wavelet coefficients from the wavelet decomposition are transformed into the wavelet energy feature vectors. 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 signals are used：normal, crackle and wheeze. The wavelet energy feature vectors with 8 dimension are reduced to 2 dimension with the presented method. The lung sounds are classified with SVM according to the two-dimensional feature vectors. 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 analysis. 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.

Key words: lung sound, linear discriminant analysis, wavelet decomposition, Support Vector Machine（SVM）