Frontal electroencephalogram analysis with ensemble empirical mode decomposition during the induction of general anesthesia

doi:10.1088/2057-1976/2/6/065004

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 多维数据分析团队

	Frontal electroencephalogram analysis with ensemble empirical mode decomposition during the induction of general anesthesia
	Tsai, Feng-Fang 1,2,3; Hu, Xiyuan3,4 ; Lin, Yi-Shiuan 1; Peng, Chung-Kang 3,5; Fan, Shou-Zen 1,2
刊名	Biomedical Physics & Engineering Express
	2016-11-30
期号	2 页码:065004
关键词	Ensemble Empirical Mode Decomposition Frontal Electroencephalogram Hilbert–huang Transformation
DOI	10.1088/2057-1976/2/6/065004
英文摘要	Background. Ensemble empiricalmode decomposition (EEMD) was proposed for decomposing electroencephalography (EEG) signals into intrinsic mode functions (IMFs), whichobtain instantaneous frequency data and work well with data that are nonstationary and nonlinear. Hilbert–Huang Transformation (HHT) was used in this study to convert IMFs into spectrograms, which are useful for observation.We recorded EEG signals through a bispectral index (BIS) monitor for EEMD analysis, and calculated the energy change after HHT. Methods. A total of 19 patients who had received general anesthesia were included. The EEG signals were recorded by physiologicalmonitor with BIS electrode strip and saved in a portal computer. The frequency changes of the IMFspectrograms were compared during the induction period, and the raw data energy changes were quantified with moving window standard deviation every 10 s. Results. The second IMF, with an initial spectrum of approximately 10–30 Hz,was focused between 10 and 15 Hz after the patient was anesthetized. All patients presented with decreased frequency and bandwidth focusing in the second IMF and indicated energy gathering. Conclusions. We found energy gathering in IMF2 after patient was anesthetized. The results suggest that examining IMFs rather than EEG signals wasmore useful for determining the particular bandwidth changes in which synchronization phenomena occur. With this method, it is easily to observe the separate energy changes of IMFs within the EEG signals.
WOS记录号	IOP:2057-1976-2-6-065004
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/19681]
专题	自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者	Fan, Shou-Zen
作者单位	1.Department of Anesthesiology, National Taiwan University Hospital 2.Department of Anesthesiology, College of Medicine, National Taiwan University 3.Rey Institute for Nonlinear Dynamics in Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School 4.Institute of Automation, Chinese Academy of Sciences 5.Center for Dynamical Biomarkers and Translational Medicine, National Central University
推荐引用方式 GB/T 7714	Tsai, Feng-Fang,Hu, Xiyuan,Lin, Yi-Shiuan,et al. Frontal electroencephalogram analysis with ensemble empirical mode decomposition during the induction of general anesthesia[J]. Biomedical Physics & Engineering Express,2016(2):065004.
APA	Tsai, Feng-Fang,Hu, Xiyuan,Lin, Yi-Shiuan,Peng, Chung-Kang,&Fan, Shou-Zen.(2016).Frontal electroencephalogram analysis with ensemble empirical mode decomposition during the induction of general anesthesia.Biomedical Physics & Engineering Express(2),065004.
MLA	Tsai, Feng-Fang,et al."Frontal electroencephalogram analysis with ensemble empirical mode decomposition during the induction of general anesthesia".Biomedical Physics & Engineering Express .2(2016):065004.