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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65954

    Title: 經驗模態分解法之清醒與麻醉情形下的腦波特徵判別
    Authors: 賴穎賢;Lai,Ying-sian
    Contributors: 機械工程學系
    Keywords: 經驗模態分解法;總體經驗模態分解法;接收者操作特徵曲線;快速傅立葉轉換
    Date: 2014-07-25
    Issue Date: 2014-10-15 17:19:06 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 本論文使用經驗模態分解法(EMD)與Matlab軟體進行病人腦電圖(EEG)分析,並且運用總體模態經驗分解法(EEMD)消除腦波量測時所受到的雜訊干擾,再搭配快速傅立葉轉換(FFT),探討各個本質模態函數(IMF)的傅立葉頻譜圖頻率差異,找出清醒與麻醉病人的腦波特徵。
    ;In this research, empirical mode decomposition (EMD) and software, such as Matlab, is used for the analysis of the patients’ electroencephalograms. In order to wipe out the disturbance caused by noise, ensemble empirical mode decomposition (EEMD) is also manipulated in the investigation. Then with the assistance of Fast Fourier Transform, the characteristics of the patients’brain wave in consciousness and anesthesia can be discovered by discussing the difference of the Fourier spectrum of each intrinsic mode function.
    The extraction of the characteristic frequency of the Fourier spectrum is divided into two sections, including the frequency of the maximum amplitude and the expected value. The frequency corresponding with the maximum amplitude of the spectrum is the frequency of the maximum amplitude. Besides, moving average is also tried to delete the disturbance caused by noise in the research. According to the analyzed data, the characteristics of the patients’brain wave in consciousness and anesthesia and the filtering effect of EEMD can be compared. In the other section, the convergence of the expected value is calculated and regarded as the characteristics of the brain waves. Subsequently, the possibility graph of the frequency distribution and receiver operating characteristic curve of each IMF are plotted. The IMFs used to identify the characteristics of consciousness and anesthesia can be revealed by exploring features of these graphs. And the identify accuracy of IMF1 is 99 percent.
    Finally, the results of these two kinds of characteristic frequency are stated and compared. The filtering effects of EMD and EEMD are also discussed.
    Appears in Collections:[機械工程研究所] 博碩士論文

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