心電圖（ECG）分析是檢測心律不整最好的方法之ㄧ，雖然已經有許多相關的演算法已經被提出，但是可靠性低的訊號特徵提取分析或歸納能力較低的辨識器使得系統的辨識率仍然不能達到要求。本論文提出適應性特徵擷取與改良的支持向量機(SVMs)的心電圖心律不整檢測系統。首先利用小波轉換係數及訊號之振幅或週期等參數作為系統的候選人，針對每一個分類器適應性的擷取出少數特定的特徵；而改良式支持向量機結合k-means分群法與一對一支持向量機，並且修改其投票機制，進一步提高了相似類別之間的辨識率。此心電圖心律不整檢測系統使用了超過100,000筆的MIT-BIH心律不整資料庫樣本進行測試，平均辨識率高達97.96%。

The electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. Many algorithms have been proposed, however, the recognition rate is still unsatisfactory due to unreliable feature extraction in signal characteristic analysis or poor generalization capability of the classifier. In this paper, we propose a system for cardiac arrhythmia detection in ECGs with adaptive feature selection and modified support vector machines (SVMs). Wavelet transform-based coefficients and signal amplitude/interval parameters are first enumerated as candidates, but only a few specific ones are adaptively selected for the classification of each class pair. A new classifier, which integrates k-means clustering, one-against-one SVMs, and a modified majority voting mechanism, is proposed to further improve the recognition rate for extremely similar classes. By testing the system with more than 100,000 samples in MIT-BIH arrhythmia database, the average recognition rate is 97.72%.

[1] 行政院衛生署 國民健康局“98年度死因統計完整統計表”。
[2] H. F. Schels, R. Habert, G. Jilge, P. Steinbigler, and G. Steinbeck, “Frequency analysis of the electrocardiogram with maximum entropy method for identification of patients with sustained ventricular tachycardia,” IEEE Trans. Biomed. Eng., vol. 38, no. 9, pp. 821-826, Sep. 1991.
[3] H. A .M.and A. L. Nashash, “A dynamic fourier series for the compression of ECG using FFT and adaptive coefficient estimation,” Medical Engineering & Physics, vol. 17, no. 3, pp. 197-203, April 1995.
[4] J. S. Paul , M. R. S. Reddy, and V. Jagadeesh Kumar, “A cepstral transformation technique for dissociation of Wide QRS type arrhythmia signals using DCT,” Signal Processing, vol. 76, pp. 123-131, 1999.
[5] I. S. N. Murthy and G. S. S. Durga Prasad, “Analysis of ECG from pole-zero models,” IEEE Trans. Biomed. Eng., vol. 39. no. 7, pp. 741-751, July 1992.
[6] C. M. Fira and L. Goras, “An ECG signals compression method and its validation using NNs,” IEEE Trans. Biomed. Eng., vol. 55. no. 4, pp. 1319-1326, April 2008.
[7] T. Stamkopoulos, K. Diamantaras, N. Maglaveras, and M. Strintzis, “ECG analysis using nonlinear PCA neural networks for ischemia detection,” IEEE Trans. Signal Processing, vol. 46. no. 11, pp. 3058-3067, Nov. 1998.
[8] P. Ranjith, P. C. Baby, and P. Joseph, “ECG analysis using wavelet transform: application to myocardial ischemia detection,” ITBM-RBM, vol. 24, no. 1, pp.44-47, Feb. 2003.
[9] N. Sivannarayana and D. C. Reddy, “Biorthogonal wavelet transforms for ECG parameters estimation,” Medical Engineering & Physics, vol. 21, no. 3, pp. 167-174, April 1999.
[10] S. Kadambe, R. Murray, and G. F. Boudreaux-Bartels, “Wavelet transform-based QRS complex detector,” IEEE Trans. Biomed. Eng., vol. 46, no. 7, pp. 838-848, July 1999.
[11] J. P. Martínez, R. Almeida,S. Olmos,A. P. Rocha, and P. Laguna, “A wavelet-based ECG delineator: evaluation on standard databases,” IEEE Trans. Biomed. Eng., vol. 51, no. 4, pp. 570-581, April 2004.
[12] A. S. Zandi and M. H. Moradi,, “Quantitative evaluation of a wavelet-based method in ventricular late potential detection,” Pattern Recognition, vol. 39, no. 7, pp. 1369-1379, July 2006.
[13] I. Güler and E. D. beyli, “ECG beat classifier designed by combined neural network model,” Pattern Recognition, vol. 38, no. 2, pp. 199-208, Feb. 2005.
[14] H. G. Hosseini, D. Luo, and K. J. Reynolds, “The comparison of different feed forward neural network architectures for ECG signal diagnosis,” Medical Engineering & Physics, vol.28, no. 4, pp. 372-378, May 2006,
[15] S. N. Yu and K. T. Chou, “Integration of independent component analysis and neural networks for ECG beat classification,” Expert Systems with Applications, vol. 34, no. 4, pp. 2841–2846, May 2008.
[16] S. N. Yu and K. T. Chou, “A switchable scheme for ECG beat classification based on independent component analysis,” Expert Systems with Applications, vol. 33, no. 4, pp. 824–829, Nov. 2007.
[17] Y. Zhu, A. Shayan, W. Zhang, T. L. Chen, T. P. Jung, J. R. Duann, S. Makeig, and C. K. Cheng, “Analyzing High-Density ECG Signals Using ICA,” IEEE Trans. Biomed. Eng., vol.55, no. 11, pp.2528-2537, Nov. 2008.
[18] A. Kampouraki, G. Manis, and C. Nikou, “Heartbeat time series classification with support vector machines,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 4, pp. 512-518, July 2009.
[19] S. Osowski, L. T. Hoai, and T. Markiewicz, “Support Vector Machine-Based Expert System for Reliable Heartbeat Recognition”, IEEE Trans. Biomed. Eng., vol. 51, no. 4, pp. 582–589, April 2004.
[20] A. H. Khandoker, M. Palaniswami, and C. K. Karmakar, “Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings”, IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 1, pp. 37–48, Jan. 2009.
[21] 陳德輝，心電圖學的原理與實用，台灣，民84。
[22] 邱毅誠，臨床心電圖學，台灣，民58。
[23] http://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm
[24] Y. Ephraim and N. Merhav, “Hidden Markov processes”, IEEE Trans. Information Theory, vol. 48, no. 6, pp. 1518-1569, Jun. 2002.
[25] A. Koski, “Modelling ECG signals with hidden Markov models”, Artificial Intelligence in Medicine, vol. 8, no. 5, pp. 453-471, Oct. 1996
[26] R. V. .Andreao, B. Dorizzi, and J. Boudy, “ECG signal analysis through hidden Markov models”, IEEE Trans. Biomed. Eng., vol.53, no.8, pp. 1541-1549, Aug. 2006.
[27] N. P. Hughes, L. Tarassenko, and S. J. Roberts, “Markov models for automated ECG interval analysis”, in Proc. NIPS 16, 2004.
[28] H. Linh, S. Osowski, and M. Stodolski, “On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network,” IEEE Trans. Instrumentation and Measurement, vol. 52, no. 4, pp.1224-1231, Aug. 2003
[29] S. Osowaki and T. H. Linh, “ECG beat recognition using fuzzy hybrid neural network,” IEEE Trans. Biomed. Eng., vol. 48, no. 11, pp. 1265-1271, Nov. 2001.
[30] N. Acir, “A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems,” Expert Systems with Applications, vol. 31, no. 2, pp. 150-158, July 2006.
[31] I. Daubechies, “The wavelet transform, time frequency localization and signal analysis,” IEEE Trans. Information Theory, vol. 36, no. 5, pp.961-1005, Sep. 1990.
[32] S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674–693, July 1989.
[33] I. Guler and E. Ubeyli, “Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats,” Expert Systems with Applications, vol. 28, no. 2, pp. 295-304, Feb. 2005.
[34] I. Güler and E. D. Übeyli, “A modified mixture of experts network structure for ECG beats classification with diverse features,” Engineering Applications of Artificial Intelligence, vol. 18, no. 7, pp. 845–856, Oct. 2005.
[35] I. Guyon, “An Introduction to Variable and Feature Selection”, Journal of Machine Learning Research, vol. 3, pp. 1157-1182, Mar. 2003.
[36] R. Jafari, H. Noshadi, S. Ghiasi, and M. Sarrafzadeh, “Adaptive Electrocardiogram Feature Extraction on Distributed Embedded Systems,” IEEE Trans. Parallel and Distributed Systems, vol. 17, no. 8, pp. 797-807, Aug. 2006.
[37] S. Theodoridis and K. Koutroumbas, Pattern Recognition 3-th ed, Elsevier, 2006.
[38] C. W. Hsu and C. J. Lin, “A comparison of methods for multi-class support vector machines,” Nat. Taiwan Univ. Taiwan. Available: http://www.csie.ntu.edu.tw/~cjlin [online].
[39] C. J. Lin, “A formal analysis of stopping criteria of decomposition methods for support vector machines,” IEEE Trans. Neural Networks, vol. 13, no. 5, pp. 1045-1052, Mar. 2002.
[40] J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. 32, no. 3, pp.220-236, Mar. 1985.
[41] H. A. Dinh, D. K. Kumar, N. D. Pah, and P. Burton, “Wavelet for QRS detection,” in Proc. 23rd Annual EMBS International Conference, pp.1883-1887, Oct. 2001.
[42] J. Lee, K. Jeong, and J. Yoon, and M. Lee, “A Simple Real-Time QRS Detection Algorithm,” in Proc. IEEE Engineering in Medicine and Biology Society, vol. 4, Nov. 1996, pp. 1396-1398.
[43] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York , 2001.
[44] R. E. Fan, P. H. Chen, and C. J. Lin, “Working set selection using second order information for training support vector machines,” Journal of Machine Learning Research, vol. 6, pp. 1889-1918, Dec. 2005.
[45] M.G. Tsipouras, D.I. Fotiadis, and D. Sideris, “An Arrhythmia Classification System Based on the RR-Interval Signal,” Artificial Intelligence in Medicine, vol. 33, no. 3, pp. 237-250, Mar. 2005.
[46] Thaler MS, The Only EKG Book You Will Ever Need, 4-th ed.’ Lippincott Williams & Wilkins, 2002.
[47] W. C. Kao, M. C. Hsu, and Y. Y. Yang, “Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition,” Pattern Recognition, vol. 43, no. 5, pp. 1736 - 1747, May 2010.
[48] C. J. Lin, “On the convergence of the decomposition method for support vector machines,” IEEE Trans. Neural Network, vol.12, no.6, pp.1288-1298, Nov. 2001.
[49] V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
[50] M. Engin, “ECG Beat Classification Using Neuro-Fuzzy Network”, Pattern Recognition Letters, vol. 25, no. 15, 1715–1722, Nov. 2004.
[51] Z.Dokur, and T.Olmez, “ECG beat classification by a novel hybrid neural network“, Computer Methods and Programs in Biomedicine, vol. 66, no. 2, pp. 167-181, Sep. 2001.
[52] 沈家平，「心電圖訊號分析演算法與硬體架構設計」，國立台灣師範大學，碩士論文，民96。