Advertisement

A Fast Method for Segmenting ECG Waveforms

  • Dipjyoti Bisharad
  • Debakshi DeyEmail author
  • Brinda Bhowmick
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)

Abstract

Electrocardiography (ECG or EKG) is a medical test that is heavily used to assess human heart condition and investigate a large set of cardiac diseases. Automated ECG analysis has become a task of increased clinical importance since it can aid physicians in improved diagnosis. Most of the automated ECG analysis techniques require first identifying the onset and offset locations of its fiducial points and characteristic waves. Two of the important characteristic waves are P and T waves. They mark the beginning and end of an ECG cycle, respectively. In this paper, a fast technique is proposed that can segment ECG signals by accurately identifying the P and T waves. In this work, we evaluate the performance of our model on standard QT database (Laguna et al. Comput Cardiol 24:673–676, 1997 [1]). We achieved high accuracies above 99% and 97% while detecting P waves and T waves respectively.

Keywords

Electrocardiogram ECG features ECG delineation ECG segmentation 

References

  1. 1.
    Laguna, P., Mark, R.G., Goldberger, A.L., Moody, G.B.: A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput. Cardiol. 24, 673–676 (1997)Google Scholar
  2. 2.
    Moody, G., Mark, R.: The impact of the MIT-BIH Arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)CrossRefGoogle Scholar
  3. 3.
    Taddei, A., Distante, G., Emdin, M., Pisani, P., Moody, G., Zeelenberg, C., Marchesi, C.: The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur. Heart J. 13, 1164–1172 (1992)CrossRefGoogle Scholar
  4. 4.
    Pan, J., Tompkins, W.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME-32, 230–236 (1985)CrossRefGoogle Scholar
  5. 5.
    Cuiwei, L., Chongxun, Z., Changfeng, T.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42, 21–28 (1995)CrossRefGoogle Scholar
  6. 6.
    Coast, D., Stern, R., Cano, G., Briller, S.: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng. 37, 826–836 (1990)CrossRefGoogle Scholar
  7. 7.
    Gritzali, F., Frangakis, G., Papakonstantinou, G.: Detection of the P and T waves in an ECG. Comput. Biomed. Res. 22, 83–91 (1989)CrossRefGoogle Scholar
  8. 8.
    Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sornmo, L.: Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47, 838–848 (2000)CrossRefGoogle Scholar
  9. 9.
    Goutas, A., Ferdi, Y., Herbeuval, J., Boudraa, M., Boucheham, B.: Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation. ITBM-RBM 26, 127–132 (2005)CrossRefGoogle Scholar
  10. 10.
    Laguna, P., Jané, R., Caminal, P.: Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput. Biomed. Res. 27, 45–60 (1994)CrossRefGoogle Scholar
  11. 11.
    Hoffman, B.F., Cranefield, P.F.: Electrophysiology of the Heart. McGraw-Hill, Blakiston Division (1960)Google Scholar
  12. 12.
    Joshi, A., Tomar, M.: A review paper on analysis of electrocardiograph (ECG) signal for the detection of arrhythmia abnormalities. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 03, 12466–12475 (2014)Google Scholar
  13. 13.
    Thakor, N., Zhu, Y.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38, 785–794 (1991)CrossRefGoogle Scholar
  14. 14.
    Hamilton, P.: Open source ECG analysis. Comput. Cardiol. IEEE, 101–104 (2002)Google Scholar
  15. 15.
    Elgendi, M., Eskofier, B., Abbott, D.: Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors 15, 17693–17714 (2015)CrossRefGoogle Scholar
  16. 16.
    Badilini, F.F.: Method and apparatus for extracting optimum Holter ECG reading. U.S. Patent 8,560,054, issued 15 Oct 2013 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dipjyoti Bisharad
    • 1
  • Debakshi Dey
    • 1
    Email author
  • Brinda Bhowmick
    • 1
  1. 1.National Institute of Technology SilcharSilcharIndia

Personalised recommendations