基于EEMD和混沌的信号特征提取方法及应用

doi:10.3969/j.issn.1000-1158.2013.02.15

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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 words： Metrology Asthma detection; Ensemble empirical mode decomposition; Intermittent chaos; Infants breathing signal

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	Articles by authors
	ZHANG Shu-qing
	DONG Xuan
	ZHAI Xin-pei
	GONG Zheng

Cite this article:

ZHANG Shu-qing,DONG Xuan,ZHAI Xin-pei, et al. A Signals Feature Extraction Method Based on the EEMD and Chaotic and Its Application[J]. Acta Metrologica Sinica, 2013, 34(2): 173-179.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2013.02.15 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2013/V34/I2/173

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