利用經驗模態分解法即時分析心電訊號

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姓名

張香治(Hsiang-Chih Chang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用經驗模態分解法即時分析心電訊號
(Real-time Analyses of Electrocardiac Signals Using Empirical Mode Decomposition)

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摘要(中)

根據行政院衛生署統計，民國九十六年國人十大死因排行榜，心臟血管疾病死亡率居第二順位。由於心臟病所引發的心臟血管疾病是無法預期，所以發展一套緊急的警告系統去即時偵測心臟疾病是很重要的，而利用心電圖(Electrocardiogram, ECG)來檢測心臟疾病為最簡便有效，且心電圖有價格便宜、即時偵測和容易取得…等優點，因此在臨床上被廣泛使用。
然而心電圖資料的量測過程中，會受到許多人為因素所造成的雜訊，這些雜訊包括來自電力線的高頻雜訊干擾、肌肉收縮引起的雜訊、由無線電所造成的高頻雜訊和移動所造成的基線飄移(Baseline Wander)…等，而這些雜訊干擾會妨礙醫師診斷上的評估。
本論文提出一個有效的ECG系統去萃取心電訊號，並使用黃鍔所提出的經驗模態分解法(Empirical Mode Decomposition, EMD)去分離周邊雜訊；以經驗模態分解為基礎的方法能夠移除與心電訊號無關的雜訊和修正基線飄移，求得一個失真率低的心電訊號。此方法利用MIT-BIH心律不整資料庫來做模擬驗證，模擬結果說明以經驗模態分解為基礎的方法，能有效的應用於移除與心電訊號無關的雜訊和修正基線飄移。並將此方法在LabVIEW平台上完成，達到即時分析處理心電訊號。

摘要(英)

According to statistic of Department of Health, Executive Yuan, R.O.C. in 2007, cardiovascular disease has been listed as the second rank of the top ten leading causes of death. Since cardiovascular disease induced heart attack may often occur in unexpected situation, development of an emergent alarm system for early detection of heart dysfunction is important. One most efficient and easy way for detection of early heart dysfunction is the use of electrocardiogram (ECG). ECG has the advantages of cheap, real-time detection, and easy to implement which been widely used in clinics. The aim of this study is to establish a noise-free real-time cardiac alarm system.
Nevertheless, ECG recordings are often corrupted by artifacts in some real practice. There are many artifacts presented in ECG recordings, including power line coupled high-frequency electricity noise, muscle contraction induced noise, high-frequency noise transmitted by surrounding RF equipments, and movement induced baseline wandering (BW). The interference of these aforementioned noises might hinder a doctor’s diagnosis and result in misleading ECG detection.
The thesis proposes a novel ECG system to exact ECG signals and separates them from surrounding noise using Dr. Huang’’s empirical mode decomposition (EMD) method. The proposed EMD-based method is able to remove ECG unrelated noise and correct BW with minimum signal distortion. The method is validated through experiments on the MIT-BIH databases. The simulations show that the proposed EMD-based method provides very good results for removing ECG unrelated noise and correcting BW. The empirical mode decomposition algorithm was also implemented on LabVIEW platform, and achieved real-time analyses of electrocardiac signals.

關鍵字(中)

★ 心電圖
★ 基線飄移
★ 經驗模態分解法

關鍵字(英)

★ Empirical Mode Decomposition (EMD)
★ Electrocardiogram (ECG)
★ Baseline Wandering (BW)

論文目次

中文摘要...........................................................................................I
英文摘要......................................................................................... II
致謝..................................................................................…………III
目錄................................................................................................IV
附圖目錄........................................................................................VI
附表目錄........................................................................................IX
第一章緒論.........................................................................……….1
1.1 前言......................................................................................... 1
1.2 研究動機...................................................................................2
1.3 文獻探討...................................................................................2
1.4 研究目的...................................................................................4
第二章心電圖原理與量測...............................................................5
2.1 心電圖.......................................................................................5
2.1.1 心電圖簡介............................................................................5
2.1.2 心電圖產生原理........................................................………..6
2.1.3 心電圖訊號............................................................................7
2.1.4 心電圖量測方式.....................................................................8
2.2 MIT-BIH 心律不整資料庫簡介.................................................11
第三章研究方法與介紹................................................................14
3.1 希爾伯特黃轉換......................................................................14
3.1.1 瞬時頻率..............................................................................15
3.1.2 內建模態函數.......................................................................16
3.1.3 經驗模態分解法...................................................................17
3.1.4 測試經驗模態分解法............................................................21
3.2 經驗模態分解法於心電訊號之應用.........................................23
3.2.1 直接利用經驗模態分解法.....................................................23
3.2.2 經驗模態分解法為基礎的演算法流程.................................. 25
3.2.3 修正心電圖訊號的基線飄移.................................................26
3.2.4 偵測心電訊號的QRS綜合波.................................................28
3.2.5 心電訊號移除QRS綜合波....................................................30
3.2.6 消除心電訊號的高頻雜訊.....................................................30
第四章實驗結果...........................................................................33
4.1 實驗設計說明..........................................................................33
4.2 模擬修正基線飄移的效果........................................................35
4.3 模擬去除高頻雜訊的效果........................................................40
4.4 實際訊號修正基線飄移的效果.................................................44
4.5 實際訊號去除高頻雜訊的效果.................................................46
4.6 實際訊號去除基線飄移與高頻雜訊的效果...............................48
4.7 利用LabVIEW 軟體即時偵測即時處理...................................50
第五章結論與未來展望................................................................52
5.1 結論........................................................................................52
5.2 未來展望.................................................................................53
參考文獻........................................................................................54

參考文獻

[1] 行政院衛生署，衛生統計資訊網，
取自http://www.doh.gov.tw/statistic/index.htm
[2] D. Benitez, P. A. Gaydecki, A. Zaidib, A. P. Fitzpatrick, “The use of the Hilbert transform in ECG signal analysis”, Computers in Biology and Medicine, vol. 31, pp. 399-406, 2001.
[3] H. C. Chen, S. W. Chen, “A moving average based filtering system with its application to real-time QRS detection”, Computers in Cardiology, pp. 585-588, 2003.
[4] 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 the Hilbert Spectrum for nonlinear and nonstationary time series analysis”, Proc. Roy. Soc. London A, vol. 454, pp. 903-995, 1998.
[5] H. Liang, Z. Lin, R. McCallum, “Artifact reduction in electrogastrogram based on empirical mode decomposition method”, Medical and Biological Engineering and Computing, vol.38, pp. 35-41, 2000.
[6] J. C. Echevarria, J. A. Crowe, M. S. Woolfson, B. R. Hayes-Gill, “Application of empirical mode decomposition to heart rate variability analysis”, Medical and Biological Engineering and Computing, vol. 39, pp. 471-479, 2001.
[7] R. Balocchi, D. Menicucci, E. Santarcangelo, L. Sebastiani, A. Gemignani, B. Ghelarducci, M. Varanini, “Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition”, Chaos, Solitons and Fractals, vol. 20, pp. 171-177, 2004.
[8] B. Weng, M. Blanco-Velasco, K. E. Barner, “Baseline wander correction in ECG by the empirical mode decomposition”, Proc. IEEE 32nd Annual Northeast Bioengineering Conf., pp. 135-136, April 2006.
[9] B. Weng, M. Blanco-Velasco, K. E. Barner, “ECG Denoising Based on the Empirical Mode Decomposition”, in Proc. 28th Annu. IEEE Int. Conf. Engineering in Medicine and Biology Society, pp. 1-4, Aug. 2006.
[10] 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, Jan. 2008.
[11] J. Malmivuo and R. Plonsey, Bioelectromagnetism - Principles and Applications of Bioelectric and Biomagnetic Fields, Oxford University Press., New York, 1995.
[12] A. C. Guyton, Human physiology and mechanisms of disease, 5th edition, W.B Saunders Company Philadelphia Pennsylvania, 1992.
[13] MIT-BIH Database Distribution,
取自http://ecg.mit.edu/
[14] L. Cohen, Time-Frequency Analysis, Prentice-Hall, Englewood Cliffs, New Jersey, 1995.
[15] E. Bedrosian, “A product theorem for Hilbert transform”, Proc. of IEEE, vol. 51, pp. 868-869, 1963.
[16] N. E. Huang, M. L. Wu, S. R. Long, et al., “A confidence limit for the Empirical Mode Decomposition and Hilbert spectral analysis”, Proc. Roy. Soc. London A, vol. 459, pp. 2317-2345, 2003.
[17] N. Pan, V. M. I, M. P. Un, P. S. Hang, “Accurate Removal of Baseline Wander in ECG Using Empirical Mode Decomposition”, Proceedings of NFSI & ICFBI, pp.177-180, 2007.
[18] 生訊科技股份有限公司，12導心電圖機，
取自http://www.biosensetek.com/productECG.html
[19] National Instruments, NI USB-6259,
取自http://sine.ni.com/nips/cds/view/p/lang/zht/nid/202598
[20] 楊正榮，「以小波轉換為基礎的QRS波偵測方法」，國立中山大學，碩士論文，民國93年。
[21] 蕭子健等編著，LabVIEW分析篇，高立圖書有限公司，台北市，民國89年。

指導教授

李柏磊(Po-Lei Lee)

審核日期

2008-7-15

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