标题:Lung sound classification based on Hilbert-Huang transform features and multilayer perceptron network
作者:Liu, Yun-Xia ;Yang, Yang ;Chen, Yue-Hui
作者机构:[Liu, Yun-Xia ;Chen, Yue-Hui ] School of Information Science and Engineering, University of Jinan, Jinan; 250022, China;[Liu, Yun-Xia ] School of Cont 更多
会议名称:9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
会议日期:12 December 2017 through 15 December 2017
来源:Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
出版年:2018
卷:2018-February
页码:765-768
DOI:10.1109/APSIPA.2017.8282137
摘要:Accurate classification of lung sounds plays an important role in noninvasive diagnosis of pulmonary diseases. A novel lung sound classification algorithm based on Hilbert-Huang transform (HHT) features and multilayer perceptron network is proposed in this paper. Three types of HHT domain features, namely the instantaneous envelope amplitude of intrinsic mode functions (IMF), envelop of instantaneous amplitude of the first four layers IMFs, and max value of the marginal spectrum are proposed for jointly characterization of the time-frequency properties of lung sounds. These proposed features are feed into a multi-layer perceptron neural network for training and testing of lung sound signal classification. Abundant experimental work is carried out to verify the effectiveness of the proposed algorithm. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050401505&doi=10.1109%2fAPSIPA.2017.8282137&partnerID=40&md5=2778ee6099a0158f4c42e372b9f6e78b
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