經驗模態分解法之清醒與麻醉情形下的腦波特徵判別

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：32

、訪客IP：3.142.197.198

姓名

賴穎賢(Ying-sian Lai) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

經驗模態分解法之清醒與麻醉情形下的腦波特徵判別

相關論文

★ 應用於車身號碼打刻機之號碼辨識	★ 複合式掌紋識別系統
★ 圓形偵測在OLED Panel 檢測上的應用	★ MLCC薄膜厚度即時線上影像檢測技術之研發
★ 全自動微鑽針影像檢測系統之研究	★ 應用類神經網路預測COG製程對於中小尺寸TFT-LCD產生之應力狀態
★ 應用機器視覺系統檢測高滲透壓刀輪切割 TFT-LCD 玻璃後斷面之研究	★ 低成本輕量化機械手臂之研究
★ 應用在同軸電纜加工之雷射光斑導引機構設計與分析	★ 表面電漿波共振-非旋轉方式的新機構設計理論
★ 網路協同式機械設計系統研發	★ 軟膠囊自動辨識系統
★ 心電訊號之擷取與分析	★ 盲人圖樣感知輔助裝置之研發設計
★ 非旋轉式表面電漿共振儀之改良與實現	★ 可攜式無線心電訊號擷取器之設計

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

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。
傅立葉頻譜圖的特徵頻率擷取可分為「最大振幅之頻率」和「期望值」兩大部分。尋找頻譜圖中最大振幅所對應的頻率，即為「最大振幅之頻率」，此外，本研究也嘗試利用移動平均來消除雜訊干擾，並且根據分析數值比較清醒與麻醉的腦波特性和EEMD濾波效果。「期望值」的部分則藉由計算傅立葉頻譜圖的收斂期望值作為腦波特徵，再繪製每個IMF的頻率分布機率圖與接收者操作特徵曲線，透過研究曲線圖特性找出能判斷清醒與麻醉特徵的IMF，其中IMF1的辨別準確性達到99%。
最後，分別介紹兩種特徵頻率的實驗結果，並比較兩種特徵頻率的優缺點和討論EMD與EEMD的濾波效果。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

論文目次

摘要 I
ABSTRACT II
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 X
第一章緒論 1
1-1前言 1
1-2研究動機 1
1-3論文架構 2
第二章腦波簡介與腦電圖 4
2-1腦波的產生 4
2-2 腦波的分類 6
2-3 腦波的量測 8
2-4干擾因素 9
第三章經驗模態分解法 11
3-1 經驗模態分解法 11
3-2 本質模態函數 14
3-3總體經驗模態分解法 15
第四章腦波資料分析方法與過程 17
4-1 腦波數據與分析平台 17
4-1-1 分析方法I-經驗模態分解法 17
4-1-2 分析方法II-總體經驗模態分解法 21
4-2 快速傅立葉轉換與特徵頻率的擷取 23
4-2-1 快速傅立葉轉換 23
4-2-2特徵頻率-最大振幅之頻率 24
4-2-3特徵頻率-期望值 27
4-3 接收者操作特徵曲線 31
4-4 整體架構與流程 35
第五章實驗結果與討論 38
5-1 特徵頻率 38
5-1-1對大振幅之頻率 38
5-1-2 期望值 45
5-2 頻率分布機率圖 47
5-2-1 經驗模態分解法-頻率分布機率圖 48
5-2-2總體經驗模態分解法-頻率分布機率圖 51
5-3 接收者操作特徵曲線 53
5-3-1經驗模態分解法-接收者操作特徵曲線 54
5-3-2總體經驗模態分解法-接收者操作特徵曲線 57
5-4 辨別性能 61
5-5 原始腦波訊號之接收者操作特徵曲線 66
第六章結論與未來展望 68
6-1 結論 68
6-2 未來展望 69
參考文獻 71

參考文獻

[1] 范守仁，「人工智慧與自動化工程在臨床醫學的應用-麻醉期間病人的
智慧型監控及醫療自動化」，國立台灣大學資訊管理學系研究所，碩士論文，July2004

[2] Ernst Niedermeyer, M.D., & Fernando Lopes Da Silva, M.D.,
Electroencephalography: Basic principles, Clinical Application, and Related Fields, Fourth Edition, Lippincott Williams & Wilkins, 1999.

[3] Mark H. Libenson,MD, Practical Approach to Electroencegraphalography,
Saunder Elsevier, 2010.

[4] Rampil, Tra J. MS, MD, “A Primer for Signal Processing in Anesthesia”, The Journal of American Society of Anesthesiologist, Inc., Vol 89(4), pp980-1002, American Society of Anesthesiologist, Inc, 1998.

[5] J.G. Webster, Electroencephalography: Brain electrical activity, Encyclopedia of Medical Devices and Instrumentation, Vol.2, 1988.

[6] 胡慕美，Ganong 生理學，合計圖書出版社，200-204頁，民國八十年。

[7] G.C. Sih, & K.K. Tang, “Dwelling Time of Normal and Abnormal Brain Waves connected with Their Transformability and Sustainability”, Theoretical and Applied Fracture Mechanics, Vol. 65, pp.34-46, Elsevier Science, 2013.

[8] N. Schaul, “The Fundamental Neural Mechanism of Electroencephalography”, Electroencephalography and Clinical Neurophysiology, Vol. 106, pp. 101-107, 1998.

[9] 關尚勇，林吉和，破解腦電波，藝軒圖書出版社，民國九十一年。

[10] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q.,
Yen, N. C., Tung, C. C. , & Liu, H. H., “The empirical mode
decomposition and the Hilbert spectrum for nonlinear and non-stationary
time series analysis,” Proceedings of Royal Society London. A, No. 454,
pp.903-995, The Royal Society, 1998.

[11] Gang Wang, Xian-Yao Chen, & Fang-Li Qiao, ”On Intrinsic Mode Function”, Advances in Adaptive Data Analysis, Vol.2, No.3, pp277-293, World Science Publishing Company, 2010.

[12] Zhaohua Wu, & Norden E. Huang, “Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method”, Advances in Adaptive Data Analysis, Vol.1, No.1, pp1-41, World Science Publishing Company, 2009.

[13] Zhaohua Wu, & Norden E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of Royal Society London. A, No. 460, pp. 1597-1611, The Royal Society, 2004.

[14] P S Mles, K Leslie, J McNeil, A Forbes, M T V Chan, “Bispectral index monitoring to prevent awareness during anaesthesia: the B-Aware randomized controlled trial”, THE LANCET, Vol.363, May 29,2004.

[15] Maan M. Shaker, “EEG Waves Classifier using Waveing Transform and Fourier Transform”, International Journal of Medical, Pharmaceutical Science and Engineering, Vol:1, NO:3, World Academy of Science and Technology,2007.

[16] Alan V.Oppenheim, & Ronald W. Schafer, Discrete-Time Signal Processing, Prentice-Hall International, Inc., 1989.

[17] Emmanuel C. Ifeachor, & Barrie W. Jervis, Digital Signal Processing:A Practical Approach, ADDISON-WESLEY,1993.

[18] E.A. Yfantis, & L.E. Borgman, ”Fast Fourier Transform 2-3-5”, Computers and Geoscience, Vol. 7, pp. 99-108, Pergamon Press Ltd, 1981.

[19] Gerald J. Hahn, &William Q. Meeker, Statistical Intervals: A Guide for Practitioners, A Wiley-Interscience Publication, 1991.

[20] Donald L. Harnett, Statistical Methods, 3rd edition, ADDISON-WESLEY Publishing Company, 1982.

[21] John A. Swet, Robyn M. Dawes, & John Monahan, “Psychological Science Can Improve Diagnostic Decisions”, Psychological Science in the Public Interest, Vol. 1, No.1, American Phychological Society, 2000.

[22] John A. Swet, “Measuring the Accuracy of Diagnostic Systems”, Scinece, New Series, Vol. 240, No 4857, pp.1285-1293, American Association for Advancement of Science, June. 3, 1988.

[23] Charles E. Metz, “Basic Principle of ROC Analysis”, Seminars in Nuclear Medicince, Vol. VIII, No. 4, October 1978.

指導教授

黃衍任、陳世叡
(Yean-ren Hwang、Shih-jui Chen)

審核日期

2014-7-25

推文