致命性的心律不整主要分為室性心動過速及心室纖維性顫動，症狀均為心室收縮失常導致心臟供血功能大幅降低，並有可能進一步惡化至心臟停搏，造成死亡。當發生致命性心律不整時須於四分鐘內對病人施以急救。由於心電圖訊號能有效的反映出心律的健康狀況，本論文提出的演算法能於短時間內根據心電圖訊號準確地分辨出非致命心律和致命性心律，並且更進一步地辨識室性心動過速及心室纖維性顫動。本論文的主要核心概念為應用小波轉換分析心電圖訊號的特性，本演算法萃取出的特徵包括常態化功率密度、波峰不規則度、波峰均值與波速。我們利用5次交叉驗證法將演算法實驗於MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB)，本方法用於分類非致命性、致命性、室性心動過速及心室纖維性顫動的靈敏度分別是97.5 %, 97.3 %, 91.7 %, 82.6 %,總體的準確率可達93.2 %。本演算法在未來的應用面可實現在健康雲端的居家看護系統上，並且能幫助醫院方面緊密地作及時性診斷，達到健康、快樂、照護等的目標。

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most lethal ventricular arrhythmias in the world. Both of them will cause serious damage to the human’s cardiovascular system. If VT or VF has been detected, it should take quick response to rescue the patients. Proposed method is based on the ECG signals to discriminate between non-lethal rhythm (N), VT and VF. The technique in the algorithm is mainly focus on Gabor Wavelet Transform (GWT) to analyze normalized power spectrum density, amplitude irregularity, mean amplitude and undulation rate of ECG signals. We test the algorithm on MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB) and obtain the result by five-fold cross validation, the sensitivity of N, VT/VF (lethal rhythm), VT and VF are 97.5 %, 97.3 %, 91.7 % and 82.6 % respectively, and the overall accuracy is 93.2 %. For the purpose of health, happiness and care, the proposed method is suitable to implement on health cloud cluster to take remote home-care, health-care of patient.In addition, the application could also help the doctor to monitor patient’s conditions, which would increase the response time for and treatment.

[1] B. Surawicz and T. Knilans, Chou's electrocardiography in clinical practice: adult and pediatric: Elsevier Health Sciences, 2008.
[2] T. C. Chan, ECG in emergency medicine and acute care: Mosby, 2005.
[3] R. E. Kerber, L. B. Becker, J. D. Bourland, R. O. Cummins, A. P. Hallstrom, M. B. Michos, et al., "Automatic External Defibrillators for Public Access Defibrillation: Recommendations for Specifying and Reporting Arrhythmia Analysis Algorithm Performance, Incorporating New Waveforms, and Enhancing Safety A Statement for Health Professionals From the American Heart Association Task Force on Automatic External Defibrillation, Subcommittee on AED Safety and Efficacy," Circulation, vol. 95, pp. 1677-1682, 1997.
[4] H. V. Huikuri, A. Castellanos, and R. J. Myerburg, "Sudden death due to cardiac arrhythmias," New England Journal of Medicine, vol. 345, pp. 1473-1482, 2001.
[5] P. Brugada, J. Brugada, L. Mont, J. Smeets, and E. W. Andries, "A new approach to the differential diagnosis of a regular tachycardia with a wide QRS complex," Circulation, vol. 83, pp. 1649-1659, 1991.
[6] P. Brugada and J. Brugada, "Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome: a multicenter report," Journal of the American College of Cardiology, vol. 20, pp. 1391-1396, 1992.
[7] B. S. Alzand and H. J. Crijns, "Diagnostic criteria of broad QRS complex tachycardia: decades of evolution," Europace, vol. 13, pp. 465-472, 2011.
[8] M. E. Curran, I. Splawski, K. W. Timothy, G. M. Vincen, E. D. Green, and M. T. Keating, "A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome," Cell, vol. 80, pp. 795-803, 1995.
[9] D. P. Zipes, A. J. Camm, M. Borggrefe, A. E. Buxton, B. Chaitman, M. Fromer, et al., "ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death)," Journal of the American College of Cardiology, vol. 48, pp. e247-e346, 2006.
[10] S. Fokkenrood, P. Leijdekkers, and V. Gay, "Ventricular tachycardia/fibrillation detection algorithm for 24/7 personal wireless heart monitoring," in International Conference on Smart Homes and Health Telematics, 2007, pp. 110-120.
[11] Q. Li, C. Rajagopalan, and G. D. Clifford, "Ventricular fibrillation and tachycardia classification using a machine learning approach," IEEE Transactions on Biomedical Engineering, vol. 61, pp. 1607-1613, 2014.
[12] A. Ibaida and I. Khalil, "Distinguishing between ventricular tachycardia and ventricular fibrillation from compressed ECG signal in wireless Body Sensor Networks," in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010, pp. 2013-2016.
[13] D. Gabor, "Theory of communication. Part 1: The analysis of information," Electrical Engineers-Part III: Radio and Communication Engineering, Journal of the Institution of, vol. 93, pp. 429-441, 1946.
[14] S. Qian and D. Chen, "Joint time-frequency analysis," IEEE Signal Processing Magazine, vol. 16, pp. 52-67, 1999.
[15] S. S. Haykin, Neural networks and learning machines vol. 3: Pearson Upper Saddle River, NJ, USA:, 2009.
[16] P. Addison, J. N. Watson, G. R. Clegg, M. Holzer, F. Sterz, and C. E. Robertson, "Evaluating arrhythmias in ECG signals using wavelet transforms," IEEE Engineering in Medicine and Biology Magazine, vol. 19, pp. 104-109, 2000.
[17] E. K. Chong and S. H. Zak, An introduction to optimization vol. 76: John Wiley & Sons, 2013.
[18] G. G. Lee, J.-Y. Hu, C.-F. Chen, and H.-H. Lin, "Gabor feature extraction for electrocardiogram signals," in 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2012, pp. 304-307.
[19] G. G. C. Lee, Z.-J. Huang, C.-Y. Chen, and C.-F. Chen, "Implementation of Gabor feature extraction algorithm for electrocardiogram on FPGA," in 2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015, pp. 798-801.
[20] P. D. Welch, "The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms," IEEE Transactions on audio and electroacoustics, vol. 15, pp. 70-73, 1967.
[21] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., "Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals," Circulation, vol. 101, pp. e215-e220, 2000.
[22] T. P. Vogl, J. Mangis, A. Rigler, W. Zink, and D. Alkon, "Accelerating the convergence of the back-propagation method," Biological cybernetics, vol. 59, pp. 257-263, 1988.
[23] M. F. Møller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural networks, vol. 6, pp. 525-533, 1993.
[24] P.-T. De Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, "A tutorial on the cross-entropy method," Annals of operations research, vol. 134, pp. 19-67, 2005.
[25] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, pp. 1137-1145.
[26] D. L. Donoho and P. B. Stark, "Uncertainty principles and signal recovery," SIAM Journal on Applied Mathematics, vol. 49, pp. 906-931, 1989.
[27] X.-S. Zhang, Y.-S. Zhu, N. V. Thakor, and Z.-Z. Wang, "Detecting ventricular tachycardia and fibrillation by complexity measure," IEEE Transactions on Biomedical Engineering, vol. 46, pp. 548-555, 1999.