Advertisement

Effective QRS-Detector Based on Hilbert Transform and Adaptive Thresholding

  • Aleksandr Aleksandrovich FedotovEmail author
  • Anna S. Akulova
  • Sergey A. Akulov
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

This paper considers the novel QRS-detector of ECG signal based on the consecutive application of band-pass filtering, Hilbert transform and adaptive thresholding. The robustness of various QRS-detectors for processing model ECG signals to the presence of intensive noises and artifacts was researched. The performance of the proposed method as well as some other established and well-known algorithms for QRS-detection was further verified for different recordings of clinical ECG signals from the Physionet MIT-BIH Arrhythmia database.

Keywords

ECG Signal QRS-wave Detection Hilbert transform Adaptive threshold 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation and clinical use (1996). Circulation 93(5):1043-1065Google Scholar
  2. 2.
    Friesen G. M et al. (1990) A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering 27(1):85–98Google Scholar
  3. 3.
    Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC. (1993) Edited by Willis J. Tompkins. Prentice Hall, New YorkGoogle Scholar
  4. 4.
    Theis F. J., Meyer-Base A. (2010) Biomedical signal analysis: Contemporary methods and applications. The MIT Press, CambridgeGoogle Scholar
  5. 5.
    Mohamed E. et al. (2010) Frequency Bands Effects on QRS Detection, Proc. of the 3rd International Conference on Bioinspired Systems and Signal Processing, Valencia, Spain, 2010, pp 428-431Google Scholar
  6. 6.
    Fedotov A. A., Akulova A. S., Akulov S. A. (2015) Analysis of the parameters of frequency filtering of an electrocardiograph signal. Springer: Measurement Techniques 57(11):1320-1325Google Scholar
  7. 7.
    Benitez D. et al (2001) The use of the Hilbert transform in ECG signal analysis. Computers in Biology and Medicine 31: 399–406Google Scholar
  8. 8.
    Rangayyan R. M. (2002) Biomedical Signal Analysis: A Case-Study Approach. IEEE Press and Wiley, New YorkGoogle Scholar
  9. 9.
    McSharry P. E. et al. (2003) A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3):289–295Google Scholar
  10. 10.
    Han H. (2007) Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography, Proc. of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, 2007, pp 1539–1541Google Scholar
  11. 11.
    Pan J., Tompkins W. J. (1985) A real time QRS detection algorithm. IEEE Transactions on Biomedical Engineering 32:230-236Google Scholar
  12. 12.
    Ruha A., Sallinen S., Nissila S. (1997) A Real-Time Microprocessor QRS Detector System with a 1-ms Timing Accuracy for the Measurement of Ambulatory HRV. IEEE Transactions on Biomedical Engineering 44(3):159–167Google Scholar
  13. 13.
    Kadambe S., Murray R. et al. (1999) Wavelet transform based QRS complex Detector. IEEE Transactions on Biomedical Engineering 46(7):838–848Google Scholar
  14. 14.
    Moody G. B., Mark R. G. (2001) The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology 20(3):45-50Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aleksandr Aleksandrovich Fedotov
    • 1
    Email author
  • Anna S. Akulova
    • 1
  • Sergey A. Akulov
    • 1
  1. 1.Department of Laser Systems and Biomedical EngineeringSamara State Aerospace UniversitySamaraRussia

Personalised recommendations