簡易檢索 / 詳目顯示

回結果列表
研究生： AMIRINNISA DYAH ATRISANDI
AMIRINNISA - DYAH ATRISANDI
論文名稱： 使用EEMD演算法濾除心電訊號的移動雜訊與準位漂移
Motion Artifact and Baseline Wander Removal Based on EEMD Algorithm for Mobile ECG
指導教授： 林淵翔
Yuan-Hsiang Lin
口試委員: 阮聖彰
Shanq-Jang Ruan
林昌鴻
Chang-Hong Lin
林敬舜
Ching-Shun Lin
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 102
中文關鍵詞： 心電圖 、運動偽影 、基準線漂移 、EEMD
外文關鍵詞： ECG, motion artifacts, baseline wander, EEMD
相關次數： 點閱：224  下載：6 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本論文提出了一個可以濾除ECG心電訊號中的移動雜訊及訊號基準線飄移的演算法，本演算法採用Ensemble Empirical Mode Decomposition (EEMD)方法，可即時地去除實驗中的高頻雜訊與人體運動產生基準線飄移的問題。我們設計了三種實驗來驗證演算法，在第一個實驗中，驗證區塊式處理(block processing)與非區塊式處理(no-block processing)的可靠度。實驗結果顯示提出的區塊處理在即時模擬實驗中，如同非區塊式處理一樣可靠；在第二個實驗中，將本演算法與一般濾波演算法及Empirical Mode Decomposition (EMD)演算法做比較。實驗結果發現本演算法優於一般濾波演算法；和EMD演算法比較，在高雜訊的情形下，本演算法優於EMD，而在低雜訊的情形下則可以得到相似的結果。另外，使用MIT-BIH資料庫中加入雜訊的訊號驗證，演算法表現的量化可由處理後的訊號的訊雜比Signal-to-Error Ratio (SER) 和Difference of Signal-to-Error Ratio (DSER)、均方誤差 (Mean Squared Error) 的增減作評估。在實際跑步時的訊號量測，此EEMD演算法表現的結果也比前面提到的EMD跟一般濾波演算法都要來的好。

    In this thesis, an algorithm to remove motion artifacts and baseline wander in ECG is proposed. The algorithm is designed based on Ensemble Empirical Mode Decomposition (EEMD) method to remove both high frequency noise and baseline drifts caused by motion artifacts. The experiments are done in three testing scenarios aimed to check the reliability of block processing and the no-block processing, and compare the performance of the proposed algorithm with general filtering and Empirical Mode Decomposition (EMD) methods. The results show that proposed algorithm in block processing scenario is reliable as the substitute of no-block processing. For performance evaluation, compared to general filtering method, the proposed algorithm performs better. In comparison with the EMD, proposed algorithm performs better in high noise level and has slightly the same performance for low noise level. These are quantitatively proven by the increasing of Signal-to-Error Ratio (SER) and Difference of Signal-to-Error Ratio (DSER) values of the noisy signals after processing and the decreasing of the Mean Squared Error (MSE) values in consequence for all MIT-BIH noise stress database signals. In real running data testing, the algorithm also shows satisfying results and perform better than the other two methods.

    Abstract ii 摘要 iii Acknowledgement iv Contents v List of Figures vii List of Tables x Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Formulation of the Problems 3 1.3 The Scope and Limitations of the Thesis 3 1.4 The Objectives and Importance of the Study 3 1.5 Thesis Organization 3 Chapter 2 Literature Reviews and Related Works 4 2.1 Electrocardiogram (ECG) 4 2.1.1 ECG Waveform 4 2.1.2 Standard ECG Recordings 5 2.1.3 Artifacts in ECG 7 2.2 Overview of ECG Recording System 8 2.3 Motion Artifacts Removal Techniques 9 2.4 Empirical Mode Decomposition 11 Chapter 3 Design and Implementation 18 3.1 Proposed Algorithm 18 3.1.1 IMFs Extraction 22 3.1.2 Artifacts Removal 24 3.2 Testing Scenarios 30 3.3 Performance Evaluation 31 Chapter 4 Results and Discussion 33 4.1 Validating sensor signal: Cardiosoft VS Mobile ECG 33 4.2 Testing Scenario 0: Block connectivity 33 4.3 Testing Scenario 1: MIT-BIH noise stress database 36 4.4 Testing Scenario 2: Sensor with real recorded noisy data 44 Chapter 5 Conclusion and Future Works 51 5.1 Summary 51 5.2 Future Works 52 References 53 Appendix A A-1 Appendix B B-1 Appendix C C-1 Appendix D D-1 Appendix E E-1

    [1] K. T. Sweeney, T. E. Ward, and S. F. McLoone. Artifact Removal in Physiological Signals—Practices and Possibilities. IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 3, May 2012
    [2] M. A. D. Raya and L. G. Sison. Adaptive noise cancelling of motion artifact in stress ECG signals using accelerometer. Proceedings of the Second Joint EMBC/BMES Conference, Houston, TX, Vol. 2, pp 1756-1757, 2002.
    [3] S. T. Lawless. Crying wolf: False alarms in a pediatric intensive care unit. Crit. Care Med., Vol. 22, No. 6, pp. 981–985, Jun. 1994.
    [4] J. Muhlsteff, O. Such, R. Schmidt, M. Perkuhn, H. Reiter, J. Lauter, J. Thijs, G. Musch, and M. Harris. Wearable approach for continuous ECG and activity patient-monitoring. Proceedings IEEE Conf. EMBS, Vol. 3, pp. 2184–2187, 2004.
    [5] J. Lee, D. D. McManus, S. Merchant, and K. H. Chon. Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches. IEEE Transactions On Biomedical Engineering, Vol. 59, No. 6, pp. 1499-1506, June 2012.
    [6] J. P. Zbilut, N. Thomasson, and C. L. Webber. Recurrence quantification analysis as a tool for nonlinear exploration of nonstationary cardiac signals. Med. Eng. Phys., pp. 53-60, 2002.
    [7] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond., 454, pp. 903–995, 1998.
    [8] K. Hung, Y. T. Zhang, and B. Tai. Wearable medical devices for tele-home healthcare. Proc. International Conf. of the IEEE EMBS, San Francisco, pp. 5384-5387, Sep., 2004.
    [9] X. F. Teng, Y. T. Zhang, C. C. Y. Poon, and P. Bonato. Wearable medical systems for p-health. IEEE Reviews in Biomedical Engineering, Vol. 1, pp. 62-74, 2008.
    [10] N. F. Chang, C. Y. Chiang, T. C. Chen, and L. G. Chen. Cubic Spline Interpolation with Overlapped Window and Data Reuse for On-line Hilbert Huang Transform Biomedical Microprocessor. Proc. International Conf. of the IEEE EMBS, Boston, pp. 7091–7094, 2011.
    [11] S. Pal and M. Mitra. Empirical mode decomposition based ECG enhancement and QRS detection. Computers in Biology and Medicine, Vol. 42, pp. 83–921, 2012.
    [12] 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.
    [13] D. U. Jeong and H. K. Jung. Development of Wearable ECG Measurement System Using EMD for Motion Artifact Removal. Proceedings of International Conf. on Computing and Convergence Technology, pp.299-304, 2012.
    [14] L. A. Yip. 2010. Master Thesis: Realtime Empirical Mode Decomposition for Intravascular Bubble Detection Modeling. School of Engineering and Physical Sciences, James Cook University, Australia.
    [15] C. C. Y. Poon and Y. T. Zhang. Some perspectives on high technologies for low-cost healthcare: Chinese scenario. IEEE Eng. Med. Biol. Mag., Vol. 27, No. 5, pp. 42–47, Sep./Oct. 2008.
    [16] Nat. Inst. Aging Nat. Inst. Health, Dept. State Dept. Health Human Services, Washington, DC. (2007). Why population aging matters: A global perspective [Online]. Available: http://www.state.gov/g/oes/rls/or/81537.htm
    [17] American Heart Association. (Mar, 2004). International Cardiovascular Disease Statistics [Online]. Available: http://www.americanheart.org
    [18] http://ecg.utah.edu/, accessed on June 14, 2014
    [19] http://rcada.ncu.edu.tw/, accessed on June 14, 2014
    [20] http://wenku.baidu.com/view/6795b080e53a580216fcfe8f.html, accessed on June 14, 2014
    [21] R. G. Mark. 2004. Clinical Electrocardiography and Arrhythmias. Dept. of Electrical Eng., Mechanical Eng., and the Harvard-MIT Div. of Health Sciences and Technology, Massachusetts Institute of Technology, USA.
    [22] K. Sweeney. 2013. Doctoral Thesis: Motion Artifact Processing Techniques for Physiological Signals. Faculty of Science and Engineering, Department of Electronic Engineering, National University of Ireland Maynooth, Ireland.
    [23] E. Lepeschkin and B. Surawicz. The measurement of the QT interval of the electrocardiogram. Circulation, Vol. 6, No. 3, pp. 378-388, 1952.
    [24] M. K. Delano. 2012. Master Thesis: A Long Term Wearable Electrocardiogram (ECG) Measurement System. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USA.
    [25] M. Arezki, A. Benallal, and D. Berkani. Improvement of the Simplified Fast Transversal Filter Type Algorithm for Adaptive Filtering. Journal of Computer Science, Vol. 5, No. 5, pp. 347-354, 2010.
    [26] M. Izzetoglu, A. Devaraj, S. Bunce, and B. Onaral. Motion artifact cancellation in NIR spectroscopy using Wiener filtering. IEEE Transactions on Biomedical Engineering, Vol. 52, No. 5, pp. 934-938, May 2005.
    [27] T. Benesty and J. Gansler. New Insights into the RLS Algorithm. EURASIP Journal on Applied Signal Processing, pp. 331-339, Mar. 2004.
    [28] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME – Journal of Basic Engineering, Vol. 82, Series D, pp. 35-45, 1960.
    [29] G. Welch and G. Bishop. An introduction to the Kalman Filter. Technical report, 2006.
    [30] P. S. Maybeck. Stochastic Models, Estimation, and Control. Mathematics in Science and Engineering, Vol. 141, 1979.
    [31] B. Azzerboni, M. Carpentieri, F. La Foresta, and F. Morabito. Neural-ICA and wavelet transform for artifacts removal in surface EMG. Proc.IEEE Int. Joint Conf. Neural Netw., Vol. 4, pp. 3223–3228, Jul. 25–29, 2004.
    [32] J. Bobin, J. L. Starck, J. M. Fadili, Y. Moudden, and D. L. Donoho. Morphological component analysis: An adaptive thresholding strategy. IEEE Transactions on Image Processing, Vol. 16, No. 11, pp. 2675-2681, Nov. 2007.
    [33] X. Yong, R. K. Ward, and G. E. Birch. Artifact removal in EEG using Morphological Component Analysis. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 345-348, Apr. 2009.
    [34] J. L. Starck, Y. Moudden, J. Bobin, M. Elad, and D. L. Donoho. Morphological Component Analysis. Proceedings of SPIE, Vol. 5914, 2005.
    [35] S. Choi, A. Cichocki, H. Park, and S. Lee. Blind Source Separation and Independent Component Analysis: A Review. Neural Information Processing - Letters and Reviews, Vol. 6, No. 1, pp.1-57, Dec. 2004.
    [36] P. R. Kowey and D. Z. Kocovic. Ambulatory Electrocardiographic Recording. Circulation. Vol. 108, pp. e31-e33, 2003.
    [37] C. Y. Chen, C. L. Chang, C. W. Chang, S. C. Lai, T. F. Chien, H. Y. Huang, J. C. Chiou, and C. H. Luo. A Low-Power Bio-Potential Acquisition System with Flexible PDMS Dry Electrodes for Portable Ubiquitous Healthcare Applications. Sensors, Vol. 13, No. 3, pp. 3077-3091, 2013.
    [38] A. Eftekhar, C. Toumazou, and E. M. Drakakis. Empirical Mode Decomposition: Real-Time Implementation and Applications. Journal of Signal Process. Syst., Vol. 73, pp. 43–58, 2013.
    [39] Y. H. Wang, C. H. Yeh, H. W. V. Young, K. Hu, and M. T. Lo. On the computational complexity of the empirical mode decomposition algorithm. Physica A, Vol. 400, pp. 59–167, 2014.
    [40] R. N. Meeson, Jr. HHT sifting and filtering. Article in Hilbert-Huang Transform and Its Applications, N. E. Huang and S. S. S. Shen, Eds. Singapore: World Scientific, pp. 75–105, 2005.
    [41] Z. Wu and N. E. Huang. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal., Vol. 1, pp. 1–41, 2009.
    [42] H. Liang, Q. H. Lin, J. D. Z. Chen. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease. IEEE Trans. Biomed. Eng., Vol. 52, No. 10, pp. 1692–1701, 2005.
    [43] G. B. Moody, W. E. Muldrow, and R. G. Mark. A noise stress test for arrhythmia detectors. Computers in Cardiology, Vol. 11, pp. 381-384, 1984.
    [44] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, Vol. 101, No. 23, pp. e215-e220.
    [45] R. Panda. 2012. Master Thesis: Removal of Artifacts from Electrocardiogram. Department of Electronics & Communication Engineering, National Institute of Technology Rourkela, India.
    [46] P.W. Macfarlane. 2010. Comprehensive Electrocardiology, Chapter 11. Springer.

    QR CODE