Call for Paper - September 2019 Edition
IJCA solicits original research papers for the September 2019 Edition. Last date of manuscript submission is August 20, 2019. Read More

A New Wavelet-based Algorithm for R-peak Detection in ECG and it’s a Comparison with the Currently Existing Algorithms

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Authors:
M. S. Dahwah, A. N. Leukhin
10.5120/ijca2019918604

M S Dahwah and A N Leukhin. A New Wavelet-based Algorithm for R-peak Detection in ECG and it’s a Comparison with the Currently Existing Algorithms. International Journal of Computer Applications 181(46):22-25, March 2019. BibTeX

@article{10.5120/ijca2019918604,
	author = {M. S. Dahwah and A. N. Leukhin},
	title = {A New Wavelet-based Algorithm for R-peak Detection in ECG and it’s a Comparison with the Currently Existing Algorithms},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2019},
	volume = {181},
	number = {46},
	month = {Mar},
	year = {2019},
	issn = {0975-8887},
	pages = {22-25},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume181/number46/30430-2019918604},
	doi = {10.5120/ijca2019918604},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The purpose of this paper is to develop a new approach for R-peak detection in ECG and compare it with the most effective and existing algorithms. The proposed approach is based on DWT and envelope for the first stage of preprocessing and decision or detecting it was achieved by adaptive thresholding. The proposed algorithm is compared with Pan & Tompkins, Savitzky-Golay smoothing filter, Hilbert and wavelet transforms as well as fast Fourier transform algorithm to investigate these techniques of R peak detection and evaluate the wavelet-based algorithm comparing with them. The algorithms are evaluated in the experimental section using ECG signals from the MIT-BIH database. The results of detection algorithms show that the proposed wavelet-based algorithm gets the highest sensitivity by 99.9% with higher reliability compared to other algorithms, also by analyzing the precision of them, it’s come to light that FFT improved the highest precision with 99.7%.

References

  1. M.A. Khayer, M.A.Haque., 2004 ECG Peak Detection using Wavelet Transform. International Conference on Electrical Computer Engineering, 518-521.
  2. J. Fraden, M.R. Neuman., 1980 QRS wave detection. Medical and Biological Engineering and Computing, 125-132.
  3. J. Pan, W. J. Tompkins., 1985 A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, 230-236.
  4. Francois Portet, Guy Carrault., 2005 Piloting real-time QRS detection algorithms in variable contexts. European Medical & Biological Engineering Conference,1-7.
  5. Okada M., 1979 A digital filter for the QRS complex detection. IEEE Trans. Biomed. Eng.,700-703.
  6. Engelse WA, Zeelenberg C., 1979 A single scan algorithm for QRS detection and feature extraction. IEEE Comput Cardiology, 37-42.
  7. Hamilton PS, Tompkins WJ., 1986 Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng., 57–65.
  8. Keselbrener L et al., 1997 Nonlinear high pass filter for R-wave detection in ECG signal. Med. Eng. Phys., 738–741.
  9. Suppappola S, Sun Y., 1994 Nonlinear transforms of ECG signals for digital QRS detection: A quantitative analysis. IEEE Trans. Biomed. Eng., 397-400.
  10. Dokur Z, Olmez T, Yazgan E, Ersoy OK., 1997 Detection of ECG waveforms by neural networks. Med. Eng. Phys., 738–741.
  11. Barro S, Fernandez-Delgado M, Vila-Sobrino JA, Regueiro CV, Sanchez E., 1998 Classifying multichannel ECG patterns with an adaptive neural network. IEEE Eng. Med. Biol. Mag., 45-55.
  12. Fernandez-Delgado M, Barro Ameneiro S., 1998 A multichannel ART-based neural network. IEEE Trans Neural Netw., 139-150.
  13. Hossein Rabbani, M. Parsa Mahjoob, E. Farahabadi, A. Farahabadi., 2011 R Peak Detection in Electrocardiogram Signal Based on an Optimal Combination of Wavelet Transform, Hilbert Transform, and Adaptive Thresholding. Journal of Medical Signals and Sensors, 91-98.
  14. L. Sathyapriya, L. Murali, T. Manigandan., 2014 Analysis and Detection R-Peak Detection using Modified Pan-Tompkins Algorithm. IEEE International Conference on Advanced Communication Control and Computing Technologies, 483-487.
  15. M. L. Ahlstrom, W. J. Tompkins., 2007 Digital Filters for Real-Time ECG Signal Processing Using Microprocessors. IEEE Transaction on Biomedical Engineering, 708-713.
  16. N. Arzeno, Z. Deng, and C. Poon., 2008 Analysis of First Derivative Based QRS Detection Algorithms. IEEE Transactions on Biomedical Engineering, 478-484.
  17. Kohler BU, Hennig C, Orlgmeister R., 2002 The principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine, 42-57.
  18. Abibullaev B, Seo H., 2011 A new QRS detection method using wavelets and artificial neural networks. Journal of Medical Systems, 683-691.
  19. Chen SW, Chen HC, Chan HL., 2006 A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Computer Methods and Programs in Biomedicine, 187-195.
  20. Zidelmal Z, Amirou A, Adnane M, Belouchrani A., 2012 QRS detection based on wavelet coefficients. Computer Methods and Programs in Biomedicine, 490-496.
  21. Mikhled Alfaouri and Khaled Daqrouq., 2008 ECG Signal Denoising By Wavelet Transform Thresholding. American Journal of Applied Sciences, 276-281.
  22. Biswas, Anamitra Bardhan Roy, Nilanjan Dey., 2012 Wavelet-based QRS Complex Detection of ECG Signal. International Journal of Engineering Research and Applications, 2361-2365.
  23. D. L. Donoho., 1995 De-noising by soft thresholding.IEEE Transaction on Information Theory, 613–627.
  24. Addison, P. S., 2010 The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC Press, Philadelphia.

Keywords

ECG, Pan & Tompkins, Savitzky-Golay smoothing filter, Fast Fourier transform, wavelet transforms, Hilbert transform, R peak.