簡單有效之心電圖信號分析及其應用於心律不整的判斷

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葉雲奇(Yun-Chi Yeh) 查詢紙本館藏

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論文名稱

簡單有效之心電圖信號分析及其應用於心律不整的判斷
(Simple and Effective QRS Complexes Detection Scheme and Its Application on Cardiac Arrhythmia Diagnosis by ECG Signals)

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摘要(中)

本篇論文包括“心電圖信號分析”及“心律不整的判斷”兩大研究主題。研究主題一是心電圖信號分析：我們提出一個簡單有效的方法稱之為差分運算法(Difference Operation Method (DOM))，它主要的目的是用來偵測心電圖(electrocardiogram (ECG)) 信號中的QRS 複合波。本主題分成下列的兩個執行步驟，分別是(1)將待測試的ECG信號作“差分運算”，其目的是要找R波；(2)以R波為基準，往R波的前面方向找Q波，往R波的後面方向找S波。以上的兩個步驟執行完後即可找到QRS 複合波，此時再繼續以現有的方法找P波與T波。最後我們以MIT-BIH 心律不整資料庫中的ECG信號來評估DOM的效能，經實際的測試，DOM比其它類似的方法有更高的正確判斷率，以及有更快的運算速度。研究主題二是心律不整的判斷：本主題我們提出了三種判斷心律不整的方法“Fuzzy Logic Method (FLM)”，“Linear Discriminant Analysis (LDA)”，及 “Fuzzy C-Means (FCM)”。這三種方法能判斷的心律包含正常的心跳 (normal heartbeats) 及不正常的心跳(abnormal heartbeats)。在本主題中，不正常的心跳包含“左束分支阻斷 (Left Bundle Branch Block)”、“右束分支阻斷(Right Bundle Branch Block)”、“心室過早收縮(Ventricular Premature Contractions)”及“心房過早收縮(Atrial Premature Contractions)”等四種較常發生的心律不整。我們以MIT-BIH 心律不整資料庫中的ECG信號來評估本主題中所提出三種判斷心律不整的方法之效能，經實際的測試，結果是：(1)處理30分鐘長的ECG信號 (約2100 次心跳) 需要的測試時間少於1分鐘(此時間包含主題一的心電圖信號分析時間)；(2)需要的記憶體空間大約是2 MB (2100 × 432 × 16 bits)；(3) FLM之正確判斷率是93.78%、 LDA 是96.23% 、FCM 是93.57%。依據以上的測試結果，結論是我們在本文中所提出的心電圖信號分析及心律不整的判斷是一個簡單有效及快速診斷的方法。

摘要(英)

In this dissertation, some novel and efficient algorithms in two related research topics about ECG signals will be presented and discussed. In the first research topic, a simple and reliable method, called the Difference Operation Method (DOM), is proposed to detect the QRS complex of an electrocardiogram (ECG) signal. The proposed DOM includes two stages. The first stage is to find the point R by applying the difference equation operation to an ECG signal. The second stage looks for the points Q and S based on the point R and then finds the whole QRS complex. From the QRS complex, both T and P waves can be obtained by the existing methods. Some records (QRS complex and T, P waves) of ECG signals in MIT-BIH arrhythmia database are tested and thus it shows that the DOM has much more precise detection rate and faster speed than other existing methods. In the second research topic, three methods, named “Fuzzy Logic Method (FLM)”, “Linear Discriminant Analysis (LDA)”, and “Fuzzy C-Means (FCM)”, are applied for classifying the cardiac arrhythmia on ECG signals. The proposed methods can accurately classify the normal heartbeats and abnormal heartbeats. Abnormal heartbeats include Left Bundle Branch Block, Right Bundle Branch Block, Ventricular Premature Contractions and Atrial Premature Contractions. The proposed methods were evaluated using the MIT-BIH arrhythmia database and have the following advantages: (1) The average time required for processing 30-minute long records of ECG signals is less than 1 minute; (2) The maximum memory requirement is only about 2 MB (that is, 2100 × 432 × 16 bits) for 30-minute long (about 2100 beats) recordings with 16 bit sampling points; (3) Good detection results (High – Reliability). In the experiments, the average failure rate for processing 30-minute long records of ECG signals is 0.19% by DOM method. The total classification accuracy is 93.78%, 96.23% and 93.57% for FLM, LDA and FCM method, respectively. Thus, the proposed methods indeed provide efficient, simple and fast methods for classifying the cardiac arrhythmia from ECG signals.

關鍵字(中)

★ 心電圖
★ 心律不整.

關鍵字(英)

★ Cardiac Arrhythmia.
★ ECG

論文目次

摘要............................................i
ABSTRACT.........................................iii
誌謝............................................v
Contents..........................................vi
List of Figures.................................viii
List of Tables....................................xi
Chapter 1 Introduction...........................1
1.1 Objective..................................1
1.2 Overview of the previous works.............2
1.2.1 The QRS complexes detection scheme..........2
1.2.2 Classifying the cardiac arrhythmia on ECG
signals.....................................3
1.3 Organization of the dissertation...........6
Chapter 2 The QRS Complex Detection of ECG Signal:
Difference Operation Method (DOM).... ..7
2.1 Review of the difference equation
operation ...................... 8
2.2 Difference Operation Method (DOM)..........8
2.3 Experiment result.........................21
Chapter 3 The Feature Selection: Range-Overlaps
Method..................................28
3.1 Overview..................................28
3.2 PQRST complex feature extraction..........29
3.3 Qualitative Feature Selection.............36
3.4 Experiment Result.........................45
Chapter 4 Classifying the Cardiac Arrhythmia
Using FLM, LDA, and FCM Methods on ECG
Signals.................................47
4.1 Fuzzy Logic Method........................47
4.2 Linear Discriminant Analysis (LDA)
method....................................54
4.2.1 Separation.................................54
4.2.2 Classification.............................57
4.3 The Fuzzy C-Means Method (FCMM)...........59
4.3.1 Review of the Fuzzy C-Means Algorithm......59
4.3.2 Procedure-FCM..............................61
4.3.3 Heartbeat case decision....................64
Chapter 5 Experiments and Evaluation............68
5.1 Experiment 1: Single heartbeat........ ....69
5.1.1 FLM method.................................69
5.1.2 LDA method.................................71
5.1.3 FCMM.......................................71
5.2 Experiment 2: Multiple heartbeats..... ....73
5.2.1 FLM Method.................................75
5.2.2 LDA method.................................76
5.2.3 FCMM.......................................77
5.3 Experiment 3: The relations between number
of used sampled vectors and the final
decision results...........................79
5.4 Experiment 4: Performance comparison......81
Chapter 6 Conclusions...........................84
References........................................86
Publication List..................................93

參考文獻

References
[1] R. M. Rangayyan, “Biomedical Signal Analysis: A Case-Study Approach,” New York: Wiley Inter-Science, 2001.
[2] J. Pan, and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., BME-32, no. 3, pp. 230-236, 1985
[3] C. W. Li, C. X. Zheng, and C. F. Tai, “Detection of ECG characteristic points using wavelet transform”, IEEE Trans. Biomed. Eng., vol. 42, no. 1, pp. 21-28, 1995
[4] D. Benitez, P.A. Gaydecki, A. Zaidi, and A.P. Fitzpatrick, “The use of the Hilbert transform in ECG signal analysis,” Computers in Biology and Medicine, vol. 31, pp. 399-406, 2001.
[5] G. Vijaya, V. Kumar, and H. K. Verma, “ANN-based QRS-complex analysis of ECG,” J. Med. Eng. Technol., vol. 22, no. 4, pp. 160-167, 1998.
[6] L. Keselbrener, M. Keselbrener, and S. Akselrod, “Nonlinear high pass filter for R-wave detection in ECG signal,” Med. Eng. Phys. vol. 19, no. 5, pp. 481-484, 1997.
[7] Z. Dokur, T. Olmez, E. Yazgan, and O. K. Ersoy, “Detection of ECG waveforms by neural networks,” Med. Eng. Phys. vol. 19, no. 8, pp. 738-741, 1997
[8] S. Kadambe, R. Murray, and G. F. Boundreaux-Bartels, “Wavelet transform-based QRS complex detector,” IEEE Trans. Biomed. Eng., vol. 46, no. 7, pp. 838-848, 1999
[9] V. X. Afonso, W. J. Tomkins, T. Q. Nguyen, and S. Luo, “ECG beat detection using filter banks,” IEEE Trans. Biomed. Eng., vol. 46, no. 2, pp. 192-202, 1999
[10] K. V. Suarez, J. C. Silva, Y. Berthoumieu, P, Gomis, and M. Najim, “ECG Beat Detection Using a Geometrical Matching Approach ,” IEEE Trans. Biomed. Eng., vol. 54, no. 4, pp. 641-650, 2007
[11] X. Xu, and Y. Liu, “ECG QRS complex detection using slope vector waveform (SVW) algorithm,” 26th Int. Conf of the IEEE EMBS, pp. 3597-3600, 2004.
[12] F. Gritzali, G. Frangakis, and G. Papakonstantinou, “Detection of the P and T waves in an ECG,” Computers and Biomedical Research, vol. 22, pp. 83-91, 1989
[13] S. J. Hengeveld, and J. H. Van Bemmel, “Computer detection of P waves”, Computers and Biomedical Research, vol. 9, pp. 125-132, 1976.
[14] R. A. Dufault and A. C. Wilcox, “Dual channel P-wave detection in the surface ECG via the LMS algorithm,” in Proc. IEEE/8th Ann. Conf. Eng. Med. Biol. Soc., pp. 325-328, 1986.
[15] Y. Zhu, and N. V. Thakor, “P-wave detection by adaptive cancellation of QRS-T complex,” in Proc. IEEE/8th Ann. Conf. Eng. Med. Biol. Soc., pp. 329-331, 1986.
[16] I. Christov, G. Gómez-Herrero, V. Krasteva, I. Jekova, A. Gotchev and K. Egiazarian, “Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification,” Med. Eng. Phys., vol. 28, pp. 876-887, 2006.
[17] P. Chazal, M. O’Dwyer, and R.B. Reilly, “Automatic classification of heart-beats using ECG morphology and heartbeat interval features,” IEEE Trans. on Biomed. Eng., vol. 51, pp. 1196-1206, 2004
[18] R. D. Throne, J. M. Jenkins, and L. A. Dicarlo, “A comparison of four new time domain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology,” IEEE Trans. on Biomed. Eng., vol. 38, pp. 561-570, 1991
[19] F. Pannizzo and S. Furman, “Frequency spectra of ventricular tachycardia and sinus rhythm in human intracardiac electrograms: Application to tachycardia for cardiac pacemakers,” IEEE Trans. on Biomed. Eng., vol. 35, pp. 421-425, 1998
[20] V.X. Afonso, W.J. Tomkins, T.Q. Nguyen, and S. Luo, “ECG beat detection using filter banks,” IEEE Trans. on Biomed. Eng., vol. 46, pp. 192-202, 1999
[21] D.Benitez, P.A. Gaydecki, A. Zaidi, and A.P. Fitzpatrick, “The use of the Hilbert transform in ECG signal analysis,” Comput. Biol. Med., vol. 31, pp. 399-406, 2001.
[22] A. Koski, “Modelling ECG signals with Hidden Markov Models,” Artif. Intel. Med., vol. 8, pp. 453-471, 1996
[23] M. Shahram and K. Nayebi, “ECG beat classification based on a Cross-Distance analysis,” International Symposium on Signal Processing and its Applications, ISSPA-2001, Malaysia, pp. 234-237.
[24] P. Laguna, R. Jane, S. Olmos, N.V. Thakor, H. Rix, and P. Caminal, “Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model,” Med. Biol. Eng. Comput., vol. 34, pp. 58-68, 1996.
[25] Z.Dokur, T.Olmez, E.Yazgan, and O.K.Ersoy, “Detection of ECG waveforms by neural networks,” Med. Eng. Phys., vol. 19, pp. 738-741, 1997.
[26] Z.Dokur, and T.Olmez, “ECG beat classification by a novel hybrid neural network“, Computer Methods and Programs in Biomedicine, vol. 66, pp. 167-181, 2001.
[27] H.G. Hosseini, D. Luo, and K.J. Reynolds, “The comparison of different feed forward neural network architectures for ECG signal diagnosis,” Med. Eng. Phys., vol.28, pp. 372-378, 2006
[28] Y.P. Meau, F. Ibrahim, S.A.L. Narainasamy, and R. Omar, “Intelligent classification of ECG signal using extended EKF based neural fuzzy system,” Computer Methods and Programs in Biomedicine, vol. 82, pp. 157-168, 2006.
[29] M. Lagerholm, G. Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo, “Clustering ECG complex using Hermite functions and self-organizing maps”, IEEE Trans. Biomed Eng., vol. 47, pp. 838-848, 2000.
[30] E. Avci and Z. H. Akpolat, “Speech recognition using a wavelet packet adaptive network based fuzzy inference system”, Expert Systems with Applications, vol. 31, pp. 495-503, 2006
[31] J. D. Wu, and T. R. Chen, “Development of a drowsiness warning system based on the fuzzy logic images analysis”, Expert Systems with Applications, vol. 34, pp. 1556-1561, 2008.
[32] A. Celikyilmaz et al. “Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions”, Expert Systems with Applications, vol. 36, pp. 1337-1354, 2009
[33] J. Yim, and H. Mitchell, “Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques”, Expert Systems with Applications, vol. 28, pp. 137-148, 2005.
[34] M. Anandarajan, and A. Anandarajan, “A comparison of machine learning techniques with a qualitative response model for auditor’s going concern reporting”, Expert Systems with Applications, vol. 16, pp. 385-392, 1999.
[35] J.C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact Well-Separated clusters”, Journal of Cybernetics, vol. 3, pp. 32-57, 1973
[36] J.C. Bezdek, “Pattern recognition with fuzzy objective function algorithms”, New York, Plenum Press, 1981
[37] J. F. Yang, S. S. Hao, and P. C. Chung, “Color image segmentation using fuzzy C-means and eigenspace projections”, Signal Processing, vol. 82, pp. 461-472, 2002
[38] A. B. Goktepe, S. Altun, and A. Sezer, “Soil clustering by fuzzy c-means algorithm”, Advances in Engineering Software, vol. 36, pp. 691-698, 2005.
[39] G. Beliakov, and M. King, “Density based fuzzy c-means clustering of non-convex patterns”, European Journal of Operational Research, vol. 173, pp. 717-728, 2006.
[40] Y.C. Yeh, and W.J. Wang, “QRS complexes detection for ECG signal: The Difference Operation method”, Computer Methods and Programs in Biomedicine, vol. 91, pp. 245-254, 2008.
[41] E. Y. Deeba, and A. D. Korvin, “On a fuzzy difference equation”, IEEE Trans. Fuzzy Systems, vol. 3, pp. 469-473, 1995.
[42] MIT-BIH database distribution, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, 1998.
[43] Z. D. Zhao, and Y. Q. Chen, “A New Method for Removal of Baseline Wander and Power Line Interference in ECG Signals”, Proceeding of the 5th International Conference on Machine Learning and Cybernetics, Dalian, vol. 13, pp. 4342-4347, 2006.
[44] 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 algorithm,” IEEE Trans. Biomed. Eng., vol. 37, no. 1, pp. 85-98, 1990
[45] P. Raphisak, S. C. Schuckers, and A. J. Curry, “An Algorithm for EMG Noise Detection in Large ECG Data”, Computers in Cardiology, vol. 31, pp. 369-372, 2004
[46] P. S. Hamilton, and M. G. Curley, “Comparison of Methods for Adaptive Removal of Motion Artifact,” Computers in Cardiology, vol. 27, pp. 383-386, 2000.
[47] B. U. Kohler, C. Henning, and R. Orglmeister, “The Principles of Software QRS Detection, IEEE Engineering in Medicine and Biology, vol. 21, no. 1, pp. 42-57, 2002.
[48] F. Pattarin”, S. Paterlini, and T. Minerva, Clustering financial time series: An application to mutual funds style analysis, Computational Statistics and Data Analysis, 2004.
[49] H. Ren, and Y. L. Chang, “Feature extraction with modified Fisher’s linear discriminant analysis”, Proc. SPIE-5995, pp. 56-62, 2005
[50] T. S. Lin, and J. Meador, “Statistical feature extraction and selection for IC test pattern analysis,” Proc. Circuits and systems, pp. 391-394, 1992.
[51] P. J. G. Lisboa, R. Mehri-Dehnavi, “Sensitivity methods for variable selection using the MLP,” International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image, pp. 330-338, 1996.
[52] M. Dash, and H. Liu, “Feature selection for classification,” Intelligent Data Analysis, vol. 1, pp. 131-156, 1997
[53] Y. Zigel, A. Cohen, and A. Katz, “The weighted diagnostic distortion (WDD) measure for ECG signal compression,” IEEE Trans. on Biomed. Eng., vol. 47, pp. 1422-1430, 2000
[54] L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility”, Fuzzy Sets and Systems, vol. 1, 1978.
[55] J. F. Hair, Jr., B. Black, B. Babin, R. E. Anderson, and R. L. Tatham, Multivariate Data Analysis, 6th ed., Prentice Hall, 2005
[56] R. A. Johnson, and D. W. Wichern, Applied Multivariate Statistical Analysis, New Jersey: Pearson Prentice Hall, 2007.
[57] W. C. Chen, and M. S. Wang, “A fuzzy c-means clustering-based fragile watermarking scheme for image authentication”, Expert System with Application, vol. 36, pp. 1300-1307, 2009
[58] I. Jekova, G. Bortolan, and I. Christov, “Assessment and comparison of different methods for heartbeat classification”, Medical Engineering and Physics, vol. 30, pp. 248-257, 2008.
[59] M. Engin, M. Fedakar, E.Z. Engin, and M. Korurek, “Feature measurements of ECG beats based on statistical classifiers”, Measurement, vol. 40, pp. 904-912, 2007

指導教授

王文俊(Wen-June Wang)

審核日期

2009-11-30

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