@Article{10798587.2016.1261957,
AUTHOR = {Qing-Hua He, Bin Yu, Xin Hong, Bo Lv, Tao Liu, Jian Ran, Yu-Tian Bi},
TITLE = {An Improved Lung Sound De-noising Method by Wavelet Packet Transform with  Pso-Based Threshold Selection},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {223--230},
URL = {http://www.techscience.com/iasc/v24n2/39748},
ISSN = {2326-005X},
ABSTRACT = {Lung abnormalities and respiratory diseases increase with the development of urban life. Lung sound 
analysis provides vital information of the present condition of the pulmonary. But lung sounds are 
easily interfered by noises in the transmission and record process, then it cannot be used for diagnosis 
of diseases. So the noised sound should be processed to reduce noises and to enhance the quality of 
signals received. On the basis of analyzing wavelet packet transform theory and the characteristics of 
traditional wavelet threshold de-noising method, we proposed a modified threshold selection method 
based on Particle Swarm Optimization (PSO) and support vector machine (SVM) to improve the quality 
of the signal, which has been polluted by noises. Experimental results show that the recognition 
accuracy of de-noised lung sounds by the improved de-noising method is 90.03%, which is much 
higher than by the other traditional de-noising methods. Meanwhile, the lung sound processed by 
the proposed method sounds better than by other methods. All results make it clear the modified 
threshold selection can obtain a better threshold vector and improve the quality of lung sounds.},
DOI = {10.1080/10798587.2016.1261957}
}