Pattern Recognition of a Digital ECG

  • Marjan GusevEmail author
  • Aleksandar Ristovski
  • Ana Guseva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 665)


The process of assisted ECG diagnosing mimics the way a medic would act upon. Such a process inevitably comprises the feature extraction step, when the standard ECG signal components: the QRS complex, the P wave and T wave are detected. Using a pattern recognition algorithm for the purpose is one of the available options. In this article, the pattern recognition approach for the feature extraction routine is explained by analysis of consecutive steps and its effectiveness is discussed in comparison to other means of QRS complex detection.


Pattern recognition QRS detection Performance engineering 


  1. 1.
    Arzeno, N.M., Deng, Z.D., Poon, C.S.: Analysis of first-derivative based QRS detection algorithms. IEEE Trans. Biomed. Eng. 55(2), 478–484 (2008)CrossRefGoogle Scholar
  2. 2.
    Blanco-Velasco, M., Weng, B., Barner, K.E.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38(1), 1–13 (2008)CrossRefGoogle Scholar
  3. 3.
    Burns, N.: Cardiovascular physiology. Retrieved from School of Medicine, Trinity College, Dublin (2013)Google Scholar
  4. 4.
    Garcia, T.B., et al.: 12-Lead ECG: The Art of Interpretation. Jones & Bartlett Publishers, Burlington (2013)Google Scholar
  5. 5.
    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  6. 6.
    Katz, L.N., Pick, A.: Clinical Electrocardiography. Lea & Febiger, Philadelphia (1956)Google Scholar
  7. 7.
    Lugovaya, T.: Biometric human identification based on electrocardiogram. Master’s thesis, Faculty of Computing Technologies and Informatics, Electrotechnical University LETI, Saint-Petersburg, Russian Federation (2005)Google Scholar
  8. 8.
    Martínez, J.P., Almeida, R., Olmos, S., Rocha, A.P., Laguna, P.: A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)CrossRefGoogle Scholar
  9. 9.
    Milchevski, A., Gusev, M.: Performance evaluation of FIR and IIR filtering of ECG signals. In: ICT Innovations 2016. Advances in Intelligent Systems and Computing, AISC, Springer series (2016, in press)Google Scholar
  10. 10.
    Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)CrossRefGoogle Scholar
  11. 11.
    Ristovski, A., Guseva, A., Gusev, M., Ristov, S.: Visualization in the ECG QRS detection algorithms. In: MIPRO, Proceedings of 39th International Convention on ICT, IEEE Conference Proceedings, pp. 218–223 (2016)Google Scholar
  12. 12.
    Trahanias, P.: An approach to QRS complex detection using mathematical morphology. IEEE Trans. Biomed. Eng. 40(2), 201–205 (1993)CrossRefGoogle Scholar
  13. 13.
    Van Alste, J., Schilder, T.: Removal of base-line wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans. Biomed. Eng. 12, 1052–1060 (1985)CrossRefGoogle Scholar
  14. 14.
    Xue, Q., Hu, Y.H., Tompkins, W.J.: Neural-network-based adaptive matched filtering for QRS detection. IEEE Trans. Biomed. Eng. 39(4), 317–329 (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Marjan Gusev
    • 1
    Email author
  • Aleksandar Ristovski
    • 2
  • Ana Guseva
    • 2
  1. 1.FCSESs. Cyril and Methodious UniversitySkopjeMacedonia
  2. 2.Innovation DooelSkopjeMacedonia

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