In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung so...In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.展开更多
In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? al...In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.展开更多
基金Funded by the International Science and Technology Cooperation Foundation of Chongqing Science and Technology Commission(Grant No.cstc2012gg-gjhz0023)the 2013 Innovative Team Construction Project of Chongqing Universities
文摘In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.
文摘In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.
