Journal of Signal Processing Systems

, Volume 90, Issue 1, pp 145–156 | Cite as

Design of a Low-Complexity Real-Time Arrhythmia Detection System

  • Robert Chen-Hao Chang
  • Hung-Lieh Chen
  • Chih-Hung Lin
  • Kuang-Hao Lin


This paper presents a low-complexity real-time arrhythmia detection system that includes the QRS complex and arrhythmia detection. The ECG data in the MIT-BIH arrhythmia database were used for simulations and verification. For QRS complex detection, this paper proposes the advanced So and Chan for detecting the R-peak and baseline of the QRS complex. Compared with the accuracy obtained using the original So and Chan method (94.61%), an accuracy of 99.29% was obtained using the advanced So and Chan method. For arrhythmia detection, the proposed system is implemented with an advanced sum of trough and various features of disease symptoms. It can identify tachycardia, bradycardia, premature contraction, and two types of cardiovascular diseases; its detection accuracy can reach 98.05%. If a morbid state occurs, a warning message will be sent to a user. Because of its low complexity, the proposed detection system can be integrated with wearable electronic devices for detecting an arrhythmia immediately.


ECG QRS complex CVD So and Chan 


  1. 1.
    Alptekin, O., & Akan, A. (2010). Detection of some heart diseases by the analysis of ECG signals. In Signal Processing and Communications Applications Conference (SIU)(pp. 716–719). 716–719.Diyarbakir, 22–24 April. doi: 10.1109/SIU.2010.5654437
  2. 2.
    Pan, J., & Tompkins, W. L. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 32(3), 230–236.CrossRefGoogle Scholar
  3. 3.
    Xue, Q., Hu, Y. H., & Tompkins, W. J. (1992). Neural-network-based adaptive matched filtering for QRS detection. IEEE Transactions on Biomedical Engineering, 39(4), 317–329.CrossRefGoogle Scholar
  4. 4.
    Hu, Y. H., Tompkins, W. J., Urrusti, J. L., & Afonso, V. X. (1993). Applications of artificial neural networks for ECG signal detection and classification. Journal of Electrocardiology, 26(1), 67–73.Google Scholar
  5. 5.
    Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering, 42(1), 21–28.CrossRefGoogle Scholar
  6. 6.
    Martínez , J. P., Olmos, S., ∓ P. Laguna, (2000). Evaluation of a wavelet-based ecg waveform detector on the qt database. In Computers in Cardiology. Los Alamitos, CA: IEEE Computer Society Press, pp. 81–84Google Scholar
  7. 7.
    Qiu, Y., Ding, X., Feng, J., & Mo, Z. (2006). QRS complexes detection based on Mexican-hat wavelet. Journal of Biomedical Engineering, 23(6), 1347–1349.Google Scholar
  8. 8.
    Chiu, C. C., Lin, T. H., & Liau, B. Y. (2005). Using correlation coefficient in ECG waveform for arrhythmia detection. Biomedical Engineering: Applications, Basis and Communications, 17(3), 147–152.Google Scholar
  9. 9.
    Fejtová, M., Macek, J., Lhotská, L. (2001) ECG events detection and classification using wavelet transform and decision trees. In Final Programme & Proceedings EUNITE, (pp 99-101). Aachen: VerlagGoogle Scholar
  10. 10.
    Andreao, R. V., Dorizzi, B., & Boudy, J. (2006). ECG signal analysis through hidden Markov models. IEEE Transactions on Biomedical Engineering, 53(8), 1541–1549.CrossRefGoogle Scholar
  11. 11.
    Gomes, P.R., Soares, F.O., Correia, J.H. Lima, C.S. (2009) Cardiac arrhythmia classification using wavelets and hidden Markov models-A comparative approach. In Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS), 4727–4730. Minneapolis, Minnesota, USA, September 2–6. doi: 10.1109/IEMBS.2009.5334192
  12. 12.
    Paul, J.S., Reddy,M.R.S., Kumar, V.J. (1997). Automatic detection of PVC’s using autoregressive models. In Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS), pp 68–71. Chicago, IL, 30 Oct.–2 Nov. doi: 10.1109/IEMBS.1997.754465.
  13. 13.
    Zhao, L., Wiggins, M., Vachtsevanos, G. (2003). Premature ventricular contraction beat detection based on symbolic dynamics analysis. In International Conference on Circuits, Signals and Systems (CSS)(pp. 48–50). Cancun, Mexico, 19-21 May.Google Scholar
  14. 14.
    Nahar, S., ShahNoor bin Munir, M. (2009). Automatic detecion of premature ventricular contraction beat using morphological transformation and cross-correlation. In International Conference on Signal Processing and Communication Systems (ICSPCS) (pp. 1–4). Omaha, NE, 28–30 Sept. doi: 10.1109/ICSPCS.2009.5306426
  15. 15.
    Lim, J. S. (2009). Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system. IEEE Transactions on Neural Networks, 20(3), 522–527.CrossRefGoogle Scholar
  16. 16.
    Shen, Z.,Hu, C.,Liao,J.,& Meng, M.Q.-H.(2010).Analgorithm of premature contraction detection based on wavelet method. In International Conference on Information and Automation (ICIA) (pp.1053– 1058). Harbin, 20 – 23 June. doi:  10.1109/ICINFA.2010.5512157
  17. 17.
    Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45–50.CrossRefGoogle Scholar
  18. 18.
    Goldberger, A. L., Amaral, L. A. N., & Glass, L. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220.CrossRefGoogle Scholar
  19. 19.
    Mark, R.G., Schluter, P.S.,Moody, G.B., Devlin, P.H., Chernoff, D. (1982). An annotated ECG database for evaluating arrhythmia detectors. In Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (pp. 205–210). IEEE Computer Society Press, Los Alamitor, CA.Google Scholar
  20. 20.
    So, H.H., Chan, K.L. (1997). Development of QRS detection method for real-time ambulatory cardiac monitor. In Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS), (pp. 289–292). Chicago, IL, 30 Oct.–2 Nov. doi: 10.1109/IEMBS.1997.754529.
  21. 21.
    Friesen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., & Nagle, H. T. (1990). A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering, 37(1), 85–98.CrossRefGoogle Scholar
  22. 22.
    Lee, R. G., Chen, C. C., Tsai, P. Y., & Yang, K. Y. (2004). A portable tele-emergent system supporting electrocardiogram (ECG) discrimination. Journal of National Taipei University Technology, 37(1), 205–213.Google Scholar
  23. 23.
    Su, S.H. (2013). Fatigue detection system using enhanced So and Chan method, Thesis of NCHUEE.
  24. 24.
    Mark, J. B. (1998). Atlas of cardiovascular monitoring (p. 130). New York: Churchill Livingstone.Google Scholar
  25. 25.
    Chang, R. C. H., Lin, C. H., Wei, M. F., Lin, K. H., & Chen, S. R. (2013). High-precision real-time premature ventricular contraction (PVC) detection system based on wavelet transform. Journal of Signal Processing Systems, 77(3), 289–296.CrossRefGoogle Scholar
  26. 26.
    Dinh, H.A.N., Kumar, D.K., Pah, N.D., Burton, P. (2001). Wavelets for QRS detection. In Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS), (1883–1887). 25-28 October 2001 Istanbul, Turkey. doi: 10.1109/IEMBS.2001.1020593.
  27. 27.
    Li, Y., Chen, X. (2011). A robust R-wave detection algorithm in ECG signal. In International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), (pp. 2433–2436). 16-18 Dec. 2011. Changchun, China. doi: 10.1109/TMEE.2011.6199713.
  28. 28.
    Pachauri, A., Bhuyan, M. (2009). Wavelet and energy based approach for PVC detection. In International Conference on Emergin Trends in Electronic and Photonic Devices & Systems, (pp. 258–261). 22–24 Dec. 2009 Varanasi. doi: 10.1109/ELECTRO.2009.5441123.
  29. 29.
    Shyu, L. Y., Wu, Y. H., & Hu, W. (2004). Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG. IEEE Transactions on Biomedical Engineering, 51(7), 1269–1273.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Robert Chen-Hao Chang
    • 1
    • 2
  • Hung-Lieh Chen
    • 1
  • Chih-Hung Lin
    • 1
  • Kuang-Hao Lin
    • 3
  1. 1.Department of Electrical EngineeringNational Chung Hsing UniversityTaichung CityRepublic of China
  2. 2.Department of Electrical EngineeringNational Chi Nan UniversityNantouRepublic of China
  3. 3.Department of Electrical EngineeringNational Formosa UniversityYunlin CountyRepublic of China

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