Skip to main content
SpringerLink
Log in

Method for Detecting Ventricular Activity of ECG Using Adaptive Threshold

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

The QRS complex in an electrocardiogram reflects the activity of the cardiac ventricles. Cardiac ventricle activity can provide information about ventricular arrhythmia. This study investigated whether the dominant activity of the ventricles can be used to analyze ventricular arrhythmia characteristics. To assess ventricular activity, we modified the adaptive threshold method proposed by Shin et al. and developed a ventricular activity segmentation approach. The proposed method was tested using five cardiac episodes, namely normal sinus rhythm, supraventricular tachycardia, ventricular tachycardia, ventricular flutter, and ventricular fibrillation, obtained from the MIT-BIH and Creighton University Ventricular Tachyarrhythmia databases. The average sensitivity was 95.77 %, the average positive predictivity was 98.09 %, and the average failed detection rate was 6.08 %.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Kohler, B. U. (2002). The principles of software QRS detection. IEEE Engineering in Medicine and Biology, 21, 42–57.

    Article  Google Scholar 

  2. Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 32, 230–236.

    Article  Google Scholar 

  3. Ferdi, Y., Herbeuval, J. P., Charef, A., & Boucheham, B. (2003). R wave detection using fractional digital differentiation. ITBM-RBM, 24, 273–280.

    Article  Google Scholar 

  4. Legarreta, I. R., Addison, P. S., Grubb, N., Clegg, G. R., Robertson, C. E., Fox, K. A. A., & Watson, J. N. (2003). R-wave detection continuous wavelet modulus maxima. Computers in Cardiology, 49, 565–568.

    Google Scholar 

  5. Fraden, J., & Neuman, M. R. (1980). QRS wave detection. Medical and Biological Engineering and Computing, 18, 125–132.

    Article  Google Scholar 

  6. Holsinger, W. P., Kempner, K. M., & Miller, M. H. (1971). A QRS preprocessor based on digital differentiation. IEEE Transactions on Biomedical Engineering, 18, 212–217.

    Article  Google Scholar 

  7. Hamilton, P. S., & Tompkins, W. J. (1986). Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Transactions on Biomedical Engineering, 33, 1157–1165.

    Article  Google Scholar 

  8. Ma, J., Shi, C., Zhang, Z., Zhu, J., Zhan, P., Fan, Y., Li, D., & Wang, L. (2014). A differentiation-based adaptive double threshold method for real time electrocardiogram R peak. Proceedings of International Conference and Biomedical Engineering Information, 3, 259–263.

    Google Scholar 

  9. Chouakri, S. A., Reguig, F. B., & Ahmed, A. T. (2011). QRS complex detection based on multi wavelet packet decomposition. Applied Math and Computation, 217, 9508–9525.

    Article  MathSciNet  MATH  Google Scholar 

  10. Mukhopadhyay, S., Biswas, S., Roy, A. B., & Dey, N. (2012). Wavelet based QRS complex detection of ECG signal. International Journal of Engineering Research and Applications, 2, 2361–2365.

    Google Scholar 

  11. Pal, S., & Mitra, M. (2010). Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method. Measurement, 43, 255–261.

    Article  Google Scholar 

  12. Bouaziz, F., Boutana, D., & Benidir, M. (2014). Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Process, 8, 774–782.

    Article  Google Scholar 

  13. Slimane, Z. E. H., & Ali, A. N. (2010). QRS complex detection using empirical mode decomposition. Digital Signal Process, 20, 1221–1228.

    Article  Google Scholar 

  14. Pal, S., & Mitra, M. (2012). Empirical mode decomposition based ECG enhancement and QRS detection. Computers in Biology and Medicine, 42, 83–92.

    Article  Google Scholar 

  15. Zong, W., Moody, G. B., & Jiang, D. (2003). A robust open-source algorithm to detect onset and duration of QRS complexes. Computers in Cardiology, 30, 737–740.

    Google Scholar 

  16. Chouhan, V. S., & Mehta, S. S. (2008). Detection of QRS complexes in 12-lead ECG using adaptive quantized threshold. International Journal of Computer Science and Network Solutions, 8, 155–163.

    Google Scholar 

  17. Ravanshad, N., Rezaee-Dehsorkh, H., & Lotfi, R. (2014). A level-crossing based QRS-detection algorithm for wearable ECG sensor. IEEE Journal of Biomedical and Health Informatics, 18, 183–192.

    Article  Google Scholar 

  18. Sun, Y., Chan, K. L., & Krishnan, S. M. (2005). Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovascular Disorders, 5, 28–34.

    Article  Google Scholar 

  19. Xue, Q., Hu, Y. H., & Tompkins, W. J. (1992). Neural-network-based adaptive matched filtering for QRS detection. IEEE Transactions on Biomedical Engineering, 39, 317–329.

    Article  Google Scholar 

  20. Mehta, S. S., Shete, D. A., Lingayat, N. S., & Chouhan, V. S. (2010). K-means algorithm for the detection and delineation of QRS-complexes in electrocardiogram. IRBM, 31, 48–54.

    Article  Google Scholar 

  21. Abibullaev, B., & Seo, H. D. (2011). A new QRS detection method using wavelets and artificial neural networks. Journal of Medical Systems, 35, 683–691.

    Article  Google Scholar 

  22. Dubin, D. (2000). Rapid interpretation of EKG’s (6th ed.). Hong Kong: Cover.

    Google Scholar 

  23. Israel, S. A., Irvine, J. M., Cheng, A., Wiederhold, M. D., & Wiederhold, B. K. (2005). ECG to identify individuals. Pattern Recognition, 38, 133–142.

    Article  Google Scholar 

  24. Papaloukas, C., Fotiadis, D. I., Likas, A., Liavas, A. P., & Michalis, L. K. (2000). A robust knowledge-based technique for ischemia detection in noisy ECGs. Proceedings of International Journal of International Conference on Knowledge-Based Engineering System and Allied Technology, 2, 768–771.

    Google Scholar 

  25. Allstot, E. G., Chen, A. Y., Dixon, A. M. R., Gangopadhyay, D., & Allstot, D. J. (2010). Compressive sampling of ECG bio-signals: Quantization noise and sparsity considerations. IEEE Biomedical Circuits and Systems Conference, 18, 41–44.

    Google Scholar 

  26. Shin, H. S., Lee, C., & Lee, M. (2009). Adaptive threshold method for the peak detection of photoplethysmographic waveform. Computers in Biology and Medicine, 39, 1145–1152.

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Ministry of Education (MOE) and National Research Foundation of Korea (NRF) through the Human Resource Training Project for Regional Innovation (Grant No. 2011H1B8A2003304).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do, 26493, Republic of Korea

    Seung Hwan Lee & Young Ro Yoon

Authors
  1. Seung Hwan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Young Ro Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young Ro Yoon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, S.H., Yoon, Y.R. Method for Detecting Ventricular Activity of ECG Using Adaptive Threshold. J. Med. Biol. Eng. 36, 410–419 (2016). https://doi.org/10.1007/s40846-016-0134-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-016-0134-z

Keywords

Search

Navigation