Advance QRS Detection Technique Incorporating Time and Amplitude Thresholds with Statistical False Peak Elimination

Authors

  • M. Kalyan Kumar  UCEK(JNTUK), Kakinada, Andhra Pradesh, India
  • Dr. K. Rama Devi  

DOI:

https://doi.org/10.32628/IJSRST52310552

Keywords:

ECG, Moving average, threshold, amplitude thresholds, Time

Abstract

In order to improve peak detection effectiveness, this work offers a brand-new peak identification technique that can reduce noise and adjust to variations in ECG signal shape. Segmentation, time and amplitude thresholds, statistical false peak elimination, median and moving average (MA) filtering, and statistical false peak elimination are the pillars upon which the suggested technique rests. An extended median filter is also used to minimize noise at an even deeper level during preprocessing, where the filters are initially employed to reduce undesired noise and interference. Using a time axis (x-axis) and an amplitude (y-axis) threshold, each segment of the data is analyzed after it has been separated into smaller sections. Next, the erroneous peaks caused by any leftover noise are removed using the average peak-to-peak interval. Any peak that is identified twice is removed, and a post- processing stage is added to check for low-amplitude peaks that were missed. The suggested methodology outperforms a number of state-of-the-art approaches in the field when tested utilizing the MIT-BIH arrhythmia and Fantasia data bases.

References

  1. Sudipta modak,Luay yassain Taha, Esam Abdel-Raheem, ‘‘ A Noval method of QRS Detectionusing Time and Amplitude Thresholds with Statistical False Peak Elimination” Received Feb 24, 2021,vol 9, accepted June 13, 2021,
  2. T. Sharma and K. K. Sharma, ‘‘QRS complex detection in ECG signals using locally adaptive weighted total variation denoising,’’ Comput. Biol. Med., vol. 87, pp. 187–199, Aug. 2017.
  3. B.-U. Kohler, C. Hennig, and R. Orglmeister, ‘‘The principles of software QRS detection,’’ IEEE Eng. Med. Biol. Mag., vol. 21, no. 1, pp. 42–57, Aug. 2002R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
  4. R. M. Rangayyan, Biomedical Signal Analysis, 33, John Wiley & sons, 2015.
  5. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng. (3) (1985) 230-236.
  6. J.D. Drake, J.P. Callaghan, Elimination of electrocardiogram contamination from electromyogram signals: an evaluation of currently used removal techniques, J. Electromyogr. Kinesiol. 16 (2) (2006) 175-187.
  7. T. Sharma and K. K. Sharma, ‘‘QRS complex detection in ECG signals using locally adaptive weighted total variation denoising,’’ Comput. Biol. Med., vol. 87, pp. 187–199, Aug. 2017.
  8. B.-U. Kohler, C. Hennig, and R. Orglmeister, ‘‘The principles of software QRS detection,’’ IEEE Eng. Med. Biol. Mag., vol. 21, no. 1, pp. 42–57, Aug. 2002.
  9. R. M. Rangayyan, Biomedical Signal Analysis. Hoboken, NJ, USA: Wiley, 2015. 
  10. X. Lu, M. Pan, and Y. Yu, ‘‘QRS detection based on improved adaptive threshold,’’ J. Healthcare Eng., vol. 2018, pp. 1 –8, Mar. 2018.
  11. J. Pan and W. J. Tompkins, ‘‘A real-time QRS detection algorithm,’’ IEEE Trans. Biomed. Eng., vol. BME-32, no. 3, pp. 230–236, Mar. 1985.
  12. S. Kadambe, R. Murray, and G. F. Boudreaux-Bartels, ‘‘Wavelet transformbased QRS complex detector,’’ IEEE Trans. Biomed. Eng., vol. 46, no. 7, pp. 838–848, Jul. 1999.
  13. P. S. Gokhale, ‘‘ECG Signal de-noising using discrete wavelet transform for removal of 50 Hz PLI noise,’’ Int. J. Adv. Res. Technol., vol. 2, no. 5, pp. 81–85, May 2012.
  14. P. Sasikala and R. Wahidabanu, ‘‘Robust R peak and QRS detection in electrocardiogram using wavelet transform,’’ Int. J. Adv. Comput. Sci. Appl., vol. 1, no. 6, pp. 48–53, 2010.
  15. S. Pal and M. Mitra, ‘‘Detection of ECG characteristic points using multiresolution wavelet analysis based selective coe fficient method,’’ Measurement, vol. 43, no. 2, pp. 255–261, Feb. 2010.
  16. Z. Zidelmal, A. Amirou, M. Adnane, and A. Belouchrani, ‘‘QRS detection based on wavelet coefficients,’’ Comput. Methods Programs Biomed., vol. 107, no. 3, pp. 490–496, Sep. 2012.
  17. H. A. N. Dinh, D. K. Kumar, N. D. Pah, and P. Burton, ‘‘Wavelets for QRS detection,’’ Australas. Phys. Eng. Sci. Med., vol. 24, no. 4, pp. 207–211, Dec. 2001.
  18. A. Sharma, S. Patidar, A. Upadhyay, and U. R. Acharya, ‘‘Accurate tunable-Q wavelet transform based method for QRS complex detection,’’ Comput. Electr. Eng., vol. 75, pp. 101–111, May 2019.
  19. S. Sahoo, P. Biswal, T. Das, and S. Sabut, ‘‘De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding,’’ Procedia Technol., vol. 25, pp. 68–75, Jan. 2016.
  20. D. Benitez, P. A. Gaydecki, A. Zaidi, and A. P. Fitzpatrick, ‘‘The use of the Hilbert transform in ECG signal analysis’’, Comput. Biol. Med., vol. 31, no. 5, pp. 399–406, Sep. 2001.

International Journal of Scientific Research in Science and Technology

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Published

2023-10-30

Issue

Volume 10, Issue 5, September-October-2023

Section

Research Articles

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How to Cite

[1]
M. Kalyan Kumar, Dr. K. Rama Devi "Advance QRS Detection Technique Incorporating Time and Amplitude Thresholds with Statistical False Peak Elimination" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 5, pp.373-381, September-October-2023. Available at doi : https://doi.org/10.32628/IJSRST52310552