研究生: |
林祥瑋 |
---|---|
論文名稱: |
利用經驗模態分解法與最小平方法消除心電訊號雜訊 Noise Filtering of Electrocardiogram Using Empirical Mode Decomposition and Least Square Method |
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: | 2009 |
畢業學年度: | 97 |
語文別: | 中文 |
論文頁數: | 76 |
中文關鍵詞: | 經驗模態分解法 、最小平方法 、心電圖訊號 、雜訊濾除 |
英文關鍵詞: | empirical mode decomposition (EMD), least squares, Electrocardiogram (ECG) signals, de-noising |
論文種類: | 學術論文 |
相關次數: | 點閱:163 下載:4 |
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心電圖在心血管相關疾病判斷與偵測上扮演相當重要的角色,而一般使用儀器量測到之心電圖通常都會受到雜訊干擾,使得心電圖上各個波形與波段的特徵變得無法辨認與擷取,這對於臨床工作者與醫療人員在病徵上的判斷會造成困擾。雜訊的種類相當多,其中不乏各個頻段的干擾源,主要如:高頻的市電干擾(Power Line Interference)、低頻的基準線漂移(Baseline Drift)與肌肉波(Electromyography, EMG)等皆為心電圖訊號中雜訊的主要來源。因此,心電圖中的雜訊濾除一直是相當重要的課題。
在本論文中,提出了使用經驗模態分解法(Empirical Mode Decomposition, EMD)與最小平方法為基礎的演算法來消除心電圖中雜訊的成分。其中,經驗模態分解法負責訊號分解的部份,經驗模態分解法最大特色就是經由篩選程序能將訊號由高頻至低頻拆解出一序列的振盪訊號,該振盪函數稱為本質模態函數(Intrinsic Mode Functions, IMFs);訊號重建的部份則是利用最小平方法準則挑選有用的本質模態函數重建無干擾訊號,並利用人為設計訊號來測試此方法的可行性,數值實驗結果驗證了本方法的優點。我們再將此方法應用於自MIT/BIH心律不整資料庫擷取之心律不整實例,並搭配一系列數位濾波器的組合來進行QRS綜合波的檢測,而模擬結果顯示了QRS綜合波的判斷上與MIT/BIH心律不整實例之病徵相吻合,也驗證了經驗模態分解法與最小平方法對於心電圖雜訊濾除的可行性。
Electrocardiogram (ECG) has played an important role to diagnose cardiovascular diseases. It often corrupted by interferences introduced by the measurement device. These interferences presented in the signal can lead to the feature of waveforms and frequency bands which can not be recognized and retrieved. These are difficulties for diagnosing symptoms of cardiovascular diseases to clinicians. There are plenty kind of interferences of ECG signals including power line interference, baseline drift and Electromyography, EMG, etc. Thus, the de-noising of ECG is an extremely significant issue. In this paper, a de-noising algorithm based on Empirical Mode Decomposition (EMD) and least square method is proposed to filter the interference of ECG signals. EMD is applied to decompose a signal into a set of oscillatory functions from high frequency to low frequency known as intrinsic mode functions (IMFs) by the sifting process. The interference-free signal is reconstructed by the selected IMFs based on least mean square criterion. Several artificial signals are used as to test the feasibility of the proposed method. Numerical results demonstrate the superiority of the proposed method. This method is also applied to some cases of Arrhythmias from the MIT/BIH Arrhythmias database. Using a set of digital filters’ combination proceed to QRS waves inspections. The simulation results show to conform QRS wave inspections to the symptoms of Arrhythmias and prove the feasibility of the proposed method for processing the ECG signals.
[1] 行政院衛生署
http://www.doh.gov.tw/cht2006/index_populace.aspx
[2] 基本心電圖圖例簡介
http://www.dcbiomed.com/material/ECG3CH.pdf
[3] J.A.V. Alste, T.S. Schilder, “Removal of Base Line Wander and Power Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps”, IEEE transactions on biomedical engineering, Vol. Bme-32. No. 12, Dec. 1985, pp.1052-1060.
[4] A. Gutierrez, P.R. Hernandez, M. Lara, S. Perez, “A QRS Detection Algorithm Based on Haar Wavelet”, Computers in Cardiology 1998 Sep. 1998, pp.353-356.
[5] A. Haar, “Zur theorie der orthogonalen funktionen-systeme”, Math. Ann., vol. 69, 1910, pp.331-373.
[6] I. Daubechies, Ten Lectures on Wavelets, SIAM, 1992.
[7] H. Xing, M. Huang, “A New QRS Detection Algorithm Based on Empirical Mode Decomposition”, Bioinformatics and biomedical engineering, May 2008, pp.693-696.
[8] D. Davis (1991), 基本心電圖判讀 (黃天守、陳清輝編譯),台北:眾文圖書股份有限公司。(原著出版於1985)
[9] http://www.adam.com/democontent/hie/images/en/1135.jpg
[10] http://static.howstuffworks.com/gif/adam/images/en/ecg-electrode-place ment-picture.jpg
[11] G.M. Friesen, T.C. Jannett, M.A. Jadallah, S.L. Yates, S.R. Quint, H.T. Nagle, “A comparison of the Noise Sensitivity of Nine QRS Detection Algorithm”, IEEE transactions on biomedical engineering, Vol. 37. No. 1. January, 1990, pp.85-98.
[12] G.D. Clifford, F. Azuaje, P.E. mcSharry, Advanced method and tools for ECG data analysis, Artech House, INC. 685 Canton Street Norwood.
[13] S.K. Mitra, Digital signal processing, McGraw.Hill international 3rd edtion 2006.
[14] A.G. Kleber, M.J. Janse, F.J. van Capelle, D. Durrer, “Mechanism and time course of S-T and T-Q segment changes during acute regional ischemia in the pig determined by extracellular and intracellular recordings”, Circ. Res., Vol. 42, 1978, pp.603-613.
[15] R. Coronel, J.R. DE Groot, M.J. Janse, “Laplacian electrograms and the interpretation of complex ventricular activation patterns during ventricular fibrillation”, J Cardiovasc Electrophysiol, Vol. 11, 2000, pp.1119-1128.
[16] N. E. Huang, Z. Shen, S. R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, 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, 1998, pp.903-995.
[17] N. E. Huang, M. C. Wu, S. R. Long, S.S.P. Shen, W. Qu, P. Gloersen, K.L. Fan, “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectrum Analysis”, Proc. R. Soc. Lond. A, Vol. 459, 2003, pp.2317-2345.
[18] P. Gonçalvés, P. Abry, G. Rilling, P. Flandrin, “Fractal Dimension Estimation: Empirical Mode Decomposition Versus Wavelets”, IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2007, Vol. 3, Apr. 2007, pp.1153-1156.
[19] V. Kamath, Y.C. Lai, L. Zhu, “Empirical Mode Decomposition and Blind Source Separation Methods for Antijamming with GPS Signals”, IEEE Position Location and Navigation Symposium PLANS 2006, Apr. 2006, pp.335-341.
[20] B. Weng, B.V. Manuel, K.E. Barner, “ECG Denoising Based on The Empirical Mode Decomposition”, IEEE International Conference on Engineering in Medicine and Biology Society EMBS 2006, 30 Aug.-3 Sep. 2006, pp.1-4.
[21] Y. Ye, J. Garcia-Casado, J.L. Martines-de-Juan, et al., “Combined Method for Artifact Reduction in Surface Eletroenterogram”, IEEE International Conference on Engineering in Medicine and Biology Society EMBS 2007, 23-26 Aug. 2007.
[22] S. Liu, Q. He, R.X. Gao, F. Patty, “Empirical Mode Decomposition Applied to Tissue Artifact Removal from Respiratory Signal”, IEEE International Conference on Engineering in Medicine and Biology Society EMBS 2008, Aug. 2008, pp.3624-3627.
[23] Md.K.I. Molla, K. Hirose, N. Minematsu, K. Hasan, “Voiced/Unvoiced Detection of Speech Signals Using Empirical Mode Decomposition Model”, IEEE International Conference on Information and Communication Technology ICICT '07, Mar. 2007, pp.311-314.
[24] K. Khaldi, A.O. Boudraa, A. Bouchikhi, M.T.H. Alouane, E.H.S Diop, ”Speech Signal Noise Reduction by EMD”, IEEE International Symposium on Communications, Control and Signal Processing ISCCSP 2008, Mar. 2008, pp.1155-1158.
[25] N. Chatlani, J.J. Soraghan, “Adaptive Empirical Mode Decomposition for Signal Enhancement with Application to Speech”, IEEE International Conference on Systems, Signals and Image Processing IWSSIP 2008, Jun. 2008, pp.101-104.
[26] R. Srinivasan, R. Rengaswamy, R. Miller, “A Modified Empirical Mode Decomposition (EMD) Process for Oscillation Characterization in Control Loops”, Control Engineering Practice, Vol. 15, No. 9, Sep. 2007, pp.1135-1148
[27] J.F. Khan, R.R. Adhami, S.M.A. Bhuiyan, K.E. Barner, “Empirical Mode Decomposition Based Interest Point Detector”, IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2008, 30 Mar. - 4 Apr. 2008, pp.1317-1320.
[28] Y.X. Huang, F.G. Schmitt, Z.M. Lu, et al., “An Amplitude-Frequency Study of Turbulent Scaling Intermittency Using Empirical Mode Decomposition and Hilbert Spectral Analysis”, A Letters Journal Exploring The Frontiers of Physics, Vol. 84, Nov. 2008, pp.1-6.
[29] V. Agarwal, L.H. Tsoukalas, “Denoising Electrical Signal via Empirical Mode Decomposition”, 2007 iREP Symposium- Bulk Power System Dynamics and Control- VII, Revitalizing Operational Reliability, Aug. 2007, pp.1-6.
[30] G. Rilling, P. Flandrin, P. Gonçalvés, “On Empirical Mode Decomposition and Its Algorithms”, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Jun. 2003.
[31] J. Cheng, D. Yu, Y. Yang, “Research on the Intrinsic Mode Function (IMF) Criterion in EMD Method”, Mechanical Systems and Signal Processing, Vol. 20, No.4, May 2006, pp.817-824.
[32] B. Xuan, Q. Xie, S. Peng, “EMD Sifting Based on Bandwidth”, IEEE Signal Processing Letters, Vol. 14, No. 8, Aug. 2007, pp.537-540.
[33] Y. Kopsinis, S. McLaughlin, “Enhanced Empirical Mode Decomposition Using a Novel Sifting-Based Interpolation Points Detection”, IEEE Workshop on Statistical Signal Processing SSP 2007, Aug. 2007, pp.725-729.
[34] Y. Kopsinis, S. McLaughlin, “Investigation of The Empirical Mode Decomposition Based on Genetic Algorithm Optimization Schemes”, IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2007, Vol. 3, Apr. 2007, pp.1397-1400.
[35] Y. Kopsinis, S. McLaughlin, “Investigation and Performance Enhancement of The Empirical Mode Decomposition on a Heuristic Search Optimization Approach”, IEEE Transaction on Signal Processing, Vol. 56, No.1, Jan. 2008, pp.1-13.
[36] Y. Kopsinis, S. McLaughlin, “Improve EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation”, EURASIP Journal on Advances in Signal Processing, Vol. 2008, Mar. 2008 , pp.1-8.
[37] Z. Zhao, Y. Wang, “A New Method for Processing End Effect in Empirical Mode Decomposition”, IEEE International Conference on Circuits and Systems for Communications ICCSC 2007, July 2007, pp.841-845.
[38] M.C. Peel, G.E. Amirthanathan, G.G.S. Pegram, “Issue with The Application of Empirical Mode Decomposition Analysis”, MODSIM 2005 International Congress on Modelling and Simulation, Dec. 2005, pp.1681-1687.
[39] G. Rilling, P. Flandrin, “One or Two Frequency? The Empirical Mode Decomposition Answers”, IEEE Transaction on Signal Processing, Vol. 56, No.1, Jan. 2008, pp.85-95.
[40] 維基百科
http://zh.wikipedia.org/wiki/%E6%9C%80%E5%B0%8F%E5%B9%B3%E6%96%B9%E6%B3%95
[41] S. Ma, K. Li, “Fast parametric time-frequency modeling of nonstationary signals”, Applied mathematics and computation 205, 2008 pp.170–177.
[42] J. Pan, W.J. Tompkins, “ A Real Time QRS detection Algorithm”, IEEE Transaction on Biomedical Engineering, Vol. Bme-32, No. 3, March 1985, pp.230-236.
[43] MIT/BIH心律不整資料庫http://www.physionet.org/physiobank/database/mitdb