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计量学报  2013, Vol. 34 Issue (2): 173-179    DOI: 10.3969/j.issn.1000-1158.2013.02.15
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基于EEMD和混沌的信号特征提取方法及应用
张淑清,董璇,翟欣沛,龚政
燕山大学电气工程学院 河北省测试与计量技术及仪器重点实验室, 河北 秦皇岛 066004
A Signals Feature Extraction Method Based on the EEMD and Chaotic and  Its Application
ZHANG Shu-qing,DONG Xuan,ZHAI Xin-pei,GONG Zheng
Institute of Electrical Engineering, Yanshan University, Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, China
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摘要 提出了一种基于集合经验模态分解(EEMD)和混沌相结合的信号特征提取方法,应用于婴儿呼吸信号哮喘检测中。EEMD把呼吸的局部信号分解成一系列频率从高到低的模态分量,对各分量与局部呼吸信号进行相关分析,并给出各分量的Hilbert谱,通过局部分析的结果初步判断婴儿是否患有哮喘;然后,以EEMD局部信号检测出来的信号频率作为混沌振子检测的频率,对全局呼吸信号进行整体检测及分析,由混沌的间歇周期可以得出原始呼吸信号的频率,准确确定婴儿哮喘诊断结果。对EEMD和混沌算法的应用存在的问题进行了改进,将其应用到实测信号的分析中,验证了方法的有效性。该方法能够正确地反映信息特征,准确率高。
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张淑清
董璇
翟欣沛
龚政
关键词 计量学哮喘检测集合经验模态分解间歇混沌婴儿呼吸信号    
Abstract:A method of feature extraction combining ensemble empirical mode decomposition (EEMD) with chaotic, and  its application  to the asthma detection in infants breathing signal are described. The partial infant breathing signal was decomposed by EEMD into a series of frequency mode components, which spread from high frequency components to low ones. The correlation between each component and partial signals was analyzed and the Hilbert spectral for major components was listed. From the partial signal, it may be generally determined whether the child suffered asthma. Then, the frequency calculated by EEMD from partial signals was chosen as the chaotic oscillator frequency to detect and analyse global breathing signals. It could express the original breathing signal frequency from the intermittent cycle of chaotic time-domain waveform, and confirm the result for infant asthma detection eventually. The EEMD and chaos algorithms were improved in order to succeed in their application. The method was put into the real data analysis and its efficiency was verified. It reflects the signals information correctly and has a high accuracy.
Key wordsMetrology    Asthma detection;     Ensemble empirical mode decomposition;     Intermittent chaos;     Infants breathing signal
    
基金资助:国家自然科学基金 (61077071,51075349);河北省自然科学基金(F2011203207)
作者简介: 张淑清(1966-),女, 山东单县人,燕山大学教授,博士,主要研究方向为弱信号检测、智能信号处理、故障诊断等。zhshq-yd@163.com
引用本文:   
张淑清,董璇,翟欣沛,龚政. 基于EEMD和混沌的信号特征提取方法及应用[J]. 计量学报, 2013, 34(2): 173-179.
ZHANG Shu-qing,DONG Xuan,ZHAI Xin-pei,GONG Zheng. A Signals Feature Extraction Method Based on the EEMD and Chaotic and  Its Application. Acta Metrologica Sinica, 2013, 34(2): 173-179.
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