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研究生： 徐士宜
Hsu Shih-Yi
論文名稱： 基於經驗模態分解法與零相位延遲濾波器之心電圖雜訊濾除法則
Electrocardiogram denoising techniques based on EMD and zero phase filter
指導教授： 吳順德
Wu, Shuen-De
學位類別： 碩士
Master
系所名稱： 機電工程學系
Department of Mechatronic Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 中文
論文頁數： 95
中文關鍵詞： 心電圖 、市電干擾 、基準線飄移 、經驗模態分解法 、零相位延遲濾波器
英文關鍵詞： Electrocardiogram, power line interference, baseline wander, empirical mode decomposition, zero-phase delay filter
論文種類： 學術論文
相關次數： 點閱：548  下載：23 
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    • 心電圖(ECG)是用來記錄病患在時間軸上，心臟的電壓變化。然而，在取得心電訊號時，訊號常會被以下三種雜訊破壞：基準線飄移、市電干擾與肌肉波干擾。為了讓醫療人員能確切的診斷出心臟疾病，因此必須將這些雜訊的影響給降低。
    近數十年來，各種濾除雜訊的方法不斷地被學者們所提出。而本研究基於經驗模態分解法(Empirical mode decomposition, EMD)與零相位延遲濾波器(Zero-phase filter)，提出兩種新的方法濾除這些雜訊。第一種濾波方式，以EMD濾波器濾除基準線飄移；接著將介於兩個QRS複合波之間的訊號，透過低通的零相位延遲濾波器濾除高頻雜訊。第二種濾波方式，根據Weng提出的方法，在處理基準線飄移的部份，改以低通的零相位延遲濾波器對原始訊號濾波，並紀錄基準線飄移的成分；當原始訊號減去基準線飄移的成分後，再經由結合杜克窗口的EMD濾波器濾除高頻雜訊。
    本研究提出的方法，其效能透過測試訊號來評估。測試訊號是由生理訊號產生器(PS-2110)先產生出心電訊號，再加入人為的虛擬雜訊。此外，本研究也使用MIT/BIH心律不整資料庫中的訊號來展示本研究方法的效能。實驗結果證實，本研究所提出的方法可有效的將各種心電雜訊濾除。

    Electrocardiogram (ECG) is the recording of the heart’s electrical potential versus time. These signals are often corrupted by three types of noise including baseline wander (BW) noise, power line interference and electromyographic (EMG) interference during the signal acquisition stage. These noise need to be attenuated in order to obtain a clean ECG signal for accurate diagnosis of heart condition.
    In the past decades, several methods have been proposed for the removal of the noise. In this dissertation, two new methods based on empirical mode decomposition (EMD) and zero-phase filter were proposed to remove the
    aforementioned artifacts. For the first proposed filtering algorithm, the baseline wandering interference is removed by EMD filter and then the power line interference between two consecutive QRS complex is removed by a low pass
    zero-phase butterworth filter. The second proposed filtering algorithm based on Weng’s algorithm estimates the baseline wandering noise by passing the original signal through a low pass zero-phase filter. The estimating baseline wandering noise is subtracted from the original signal and then the high frequency interference is removed from the signal by using the EMD filter with a Turkey window.
    The performance of the proposed algorithms is evaluated on the data sets which are generated from a physical ECG signal generator (PS-2110) with an artificial noise. Moreover, MITBIH database are also used to demonstrate the
    efficiency of the proposed filtering algorithms. Experimental results indicate that the interferences of ECG can be removed effectively by the proposed algorithms.

    致謝 I 中文摘要 II 英文摘要 III 目錄 IV 圖目錄 VII 表目錄 IX 附錄 1 X 附錄 2 XI 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目的 1 1.3 論文架構 3 第二章 心電圖概論 4 2.1 心電訊號的傳導 4 2.2 心電圖十二導程 6 2.2.1 標準誘導 6 2.2.2 加壓誘導 8 2.2.3 胸前誘導 8 2.2.4 標準心電圖波形 10 2.3 心電訊號的干擾 10 2.4 心臟疾病之ECG訊號 12 2.5 波形檢測與病徵關係 15 2.5.1 QRS波形檢測 15 2.5.2 T波與P波偵測 17 2.5.3 疾病與心電圖特徵關係 19 2.6 結語 19 第三章 相關分析理論與濾波器 20 3.1 經驗模態分解法 20 3.1.1 演算法 20 3.1.2 本質模態函數 21 3.1.3 篩選程序 21 3.1.4 停止準則 23 3.1.5 EMD濾波器 25 3.2 數位濾波器 26 3.2.1 IIR與FIR概論 26 3.2.2 零相位延遲濾波器 28 3.3 文獻回顧 28 3.3.1 Zhao與Chen方法 29 3.3.2 Weng與Barner方 30 3.4 結語 34 第四章 ECG濾波法 35 4.1 測試訊號源 35 4.1.1 生理訊號產生器 35 4.1.2 MIT/BIH心律不整資料庫 36 4.1.3 雜訊參雜 37 4.2 濾波方法一 38 4.2.1 消除基準線飄移 40 4.2.2 保留QRS複合波 41 4.2.3 移除相對高頻雜訊 42 4.2.4 考慮不連續點 43 4.3 濾波方法二 44 4.3.1 高頻雜訊處理 44 4.3.2 移除基準線飄移 45 4.4 結語 46 第五章 實驗結果 47 5.1 性能指標 47 5.1.1 相關性與均方根誤差 47 5.1.2 訊號雜訊比與訊號誤差比 49 5.2 心電圖濾波結果展示(生理訊號產生器) 49 5.3 心電圖濾波結果展示(MIT/BIH心律不整資料庫) 50 5.4 方法一的不連續點處裡 51 第六章 總結 53 6.1 結論 53 6.2 問題說明 53 6.3 未來工作 55 參考文獻 57

    [1] S. Poornachandra, “Wavelet-Based Denoising Using Subband Dependent Threshold for ECG Signals,” Digital Signal Processing, Vol.18, pp. 49-55, 2008.
    [2] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. Tung, and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis,” Proc. R. Soc. Lond. A , Vol. 454, pp. 903-995, 1998.
    [3] B. Weng, M. Blanco-Velasco, and K. E. Barner, “Baseline Wander Correction in ECG by the Empirical Mode Decomposition,” Bioengineering Conference, 2006. Proceedings of the IEEE 32nd Annual Northeast , pp. 135-136, 2006.
    [4] T. Y. Ji, Z. Lu, Q. H. Wu, and Z. Ji, “Baseline Normalisation of ECG Signals Using Empirical Mode Decomposition and Mathematical Morphology,” Electronics Letters , Vol. 44, pp. 82-83, 2008.
    [5] M. G. Frei and I. Osorio, “Intrinsic Time-Scale Decomposition: Time-Frequency-Energy Analysis and Real-Time Filtering of Non-Stationary Signals,” Proc. R. Soc. A, Vol. 463, pp. 321-342, 2007.
    [6] G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, “A Comparison of the Noise Sensitivity of Nine QRS Detection Algorithms,” IEEE Transactions on Biomedical Engineering, Vol. 37, pp. 85-98, 1990.
    [7] J. Pan and W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, Vol. BME-32, pp. 230-236, 1985.
    [8] P. S. Hamilton and W. J. Tompkins, “Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database,” IEEE Transactions on Biomedical Engineering, Vol. BME-33, pp. 1157-1165, 1986.
    [9] A. J. Nimunkar and W. J. Tompkins, “EMD-Based 60-Hz Noise Filtering of the ECG,” Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE , pp. 1904-1907, 2007.
    [10] Z. D. Zhao and Y. Q. Chen, “A New Method for Removal of Baseline Wander and Power Line Interference in ECG Signals,” Machine Learning and Cybernetics, 2006 International Conference on , pp. 4342-4347, 2006.
    [11] O. T. Inan and G. T. A. Kovacs, “An 11 W, Two-Electrode Transimpedance Biosignal Amplifier with Active Current Feedback Stabilization,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 4, pp. 93-100, 2010.
    [12] T. Degen and H. Jackel, “Continuous Monitoring of Electrode-Skin Impedance Mismatch During Bioelectric Recordings,” IEEE Transactions on Biomedical Engineering, Vol. 55, pp. 1711-1715, 2008.
    [13] A. K. Ziarani and A. Konrad, “A Nonlinear Adaptive Method of Elimination of Power Line Interference in ECG Signals,” IEEE Transactions on Biomedical Engineering, Vol.49, pp. 540-547, 2002.
    [14] S. M. M. Martens, M. Mischi, S. G. Oei, and J. W. M. Bergmans, “An Improved Adaptive Power Line Interference Canceller for Electrocardiography,” IEEE Transactions on Biomedical Engineering, Vol. 53, pp. 2220-2231, 2006.
    [15] R. Ramos, A. Manuel-Lazaro, J. Del Rio, and G. Olivar, “FPGA-Based Implementation of an Adaptive Canceller for 50/60-Hz Interference in Electrocardiography,” IEEE Transactions on Instrumentation and Measurement, Vol. 56, pp. 2633-2640, 2007.
    [16] M. Shao, K. E. Barner, and M. H. Goodman, “An Interference Cancellation Algorithm for Noninvasive Extraction of Transabdominal Fetal Electroencephalogram (TaFEEG),” IEEE Transactions on Biomedical Engineering, Vol. 51, pp. 471-483, 2004.
    [17] O. Sayadi and M. B. Shamsollahi, “Model-Based Fiducial Points Extraction for Baseline Wandered Electrocardiograms,” IEEE Transactions on Biomedical Engineering , Vol. 55, pp. 347-351, 2008.
    [18] Z. Fei and L. Yong, “QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 3, pp. 220-228, 2009.
    [19] P. Sun, Q. H. Wu, A. M. Weindling, A. Finkelstein, and K. Ibrahim, “An Improved Morphological Approach to Background Normalization of ECG Signals,” IEEE Transactions on Biomedical Engineering, Vol. 50, pp. 117-121, 2003.
    [20] M. Ferdjallah and R. E. Barr, “Adaptive Digital Notch Filter Design on the Unit Circle for the Removal of Powerline Noise from Biomedical Signals,” IEEE Transactions on Biomedical Engineering, Vol. 41, pp. 529-536, 1994.
    [21] M. L. Ahlstrom and W. J. Tompkins, “Digital Filters for Real-Time ECG Signal Processing Using Microprocessors,” IEEE Transactions on Biomedical Engineering, Vol. BME-32, pp. 708-713, 1985.
    [22] S. C. Pei and C. C. Tseng, “Elimination of AC Interference in Electrocardiogram Using IIR Notch Filter with Transient Suppression,” IEEE Transactions on Biomedical Engineering, Vol. 42, pp. 1128-1132, 1995.
    [23] P. S. Hamilton, “A Comparison of Adaptive and Nonadaptive Filters for Reduction of Power Line Interference in the ECG,” IEEE Transactions on Biomedical Engineering, Vol. 43, pp. 105-109, 1996.
    [24] Y. D. LIN and H. Y. HEN, “Power-Line Interference Detection and Suppression in ECG Signal Processing,” IEEE Transactions on Biomedical Engineering, Vol. 55, pp. 354-357, 2008.
    [25] I. P. Mitov, “A Method for Reduction of Power Line Interference in the ECG,” Medical Engineering & Physics, Vol. 26, pp. 879-887, 2004.
    [26] S. Poornachandra and N. Kumaravel, “A Novel Method for the Elimination of Power Line Frequency in ECG Signal Using Hyper Shrinkage Function,” Digital Signal Processing, Vol. 18, pp. 116-126, 2008.
    [27] E. Ercelebi, “Electrocardiogram Signals De-Noising Using Lifting-Based Discrete Wavelet Transform,” Computers in Biology and Medicine, Vol. 34, pp. 479-493, 2004.
    [28] S. Poornachandra and N. Kumaravel, “Hyper-Trim Shrinkage for Denoising of ECG Signal,” Digital Signal Processing, Vol. 15, pp. 317-327, 2005.
    [29] 黃天守，陳清輝譯，“基本心電圖判讀”，眾文圖書股份有限公司，(第一版)，原著：Dale Davis, How to Quickly and Accurately Master ECG Interpretation，1997.
    [30] H. C. Bazett, “An Analysis if the Time-Relations of Electrocardiograms,” Heart(7):353:370, 1920.
    [31] 邱國元，「十二導程心電圖機研製」，國立交通大學，碩士論文，民國96年9月。
    [32] G. Rilling, P. Flandrin, and P. Gonçalvés, “On Empirical Mode Decomposition and Its Algorithms,” IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado, Italy, 2003.
    [33] 陳思予，「改良式本質模態分解法在訊號處理之應用」，國立台灣師範大學，碩士論文，民國98年6月。
    [34] K. M. Sanjit, “Digital Signal Processing,” 3rd Ed., McGRAW.Hill International Edition, 2006.
    [35] J. M. Leski and N. Henzel, “ECG Baseline Wander and Powerline Interference Reduction Using Nonlinear Filter Bank,” Signal Processing, Vol. 85, pp. 781-793, 2005.
    [36] K. C. Lai and J. J. Shynk, “A Successive Cancellation Algorithm for Fetal Heart-Rate Estimation Using an Intrauterine ECG Signal,” IEEE Transactions on Biomedical Engineering, Vol. 49, pp. 943-954, 2002.
    [37] R. Jane, P. Laguna, N. V. Thakor, and P. Caminal, “Adaptive Baseline Wander Removal in the ECG: Comparative Analysis with Cubic Spline Technique,” Computers in Cardiology 1992. Proceedings., pp. 143-146, 1992.
    [38] H. C. So, “A New Adaptive Algorithm for Eliminating Sinusoidal Interferences,” Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on , pp. 514-517, 1998.
    [39] V. Shusterman, S. I. Shah, A. Beigel, and K. P. Anderson, “Enhancing the Precision of ECG Baseline Correction: Selective Filtering and Removal of Residual Error,” Computers and Biomedical Research, Vol. 33, pp. 144-160, 2000.
    [40] S. Canan, Y. Ozbay, and B. Karlik, “A Method for Removing Low Varying Frequency Trend from ECG Signal,” Biomedical Engineering Days, 1998. Proceedings of the 1998 2nd International Conference , pp. 144-146, 1998.
    [41] N. V. Thakor and Y. S. Zhu, “Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection,” IEEE Transactions on Biomedical Engineering, Vol. 38, pp. 785-794, 1991.
    [42] B. Weng, M. Blanco-Velasco, and K. E. Barner, “ECG Denoising Based on the Empirical Mode Decomposition,” Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE, pp. 1-4, 2006.
    [43] M. Blanco-Velasco, B. Weng, and K. E. Barner, “ECG Signal Denoising and Baseline Wander Correction Based on the Empirical Mode Decomposition,” Computers in Biology and Medicine, Vol. 38, pp. 1-13, 2008.
    [44] 呂雅婷，「脊髓損傷病患其心率變異與起坐性低血壓之關係」，私立中原大學，碩士論文，民國90年。

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