A Reliable QRS Detection Method Based on Dual-Tree Wavelet Transform

  • Oussama EL B'charri ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco
  • Rachid Latif ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco
  • Abdenbi Abenaou ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco
  • Khalifa Elmansouri High School of Biomedical Engineering; UM6SS University, Casablanca, Morocco
  • Wissam Jenkal ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco
Keywords: ECG signal, QRS complex, dual tree wavelet transform

Abstract

Electrocardiography is considered as a powerful technique for assessing heart condition. To study cardiac disorders, it is essential to localize and extract the QRS complex: the prominent region within the electrocardiogram signal. Since the QRS complex has various morphologies and is usually contaminated by severe overlapping spectral noise, accurate detection is a complicated task. This paper proposes a reliable method based on the Dual-Tree Wavelet Transform, which uses a threshold process to select the QRS frequency components and reduce the overlapping noise. The QRS deflections are then emphasized using squaring and moving average operators. The chosen decision rule is simple and based on the variance of the signal. The proposed method was tested on the MIT-BIH Arrhythmia database, and the algorithm showed high accuracy detection results compared to those of other recently published works.

References

myVMC, “ECG (electrocardiogram) heartbeat monitoring information,” https://www.myvmc.com/investigations/ecg-ekg-electrocardiogram/, December 19, 2017.

F. Morris, W. J. Brady, and A. J. Camm, ABC of clinical electrocardiography, 2nd ed. Chicester: John Wiley & Sons, June 2008.

J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230-236, March 1985.

P. S. Hamilton and W. J. Tompkins, “Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database,” IEEE Transactions on Biomedical Engineering, vol. 33, no. 12, pp. 1157-1165, December 1986.

S. Yazdani and J. M. Vesin, “Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology,” Digital Signal Processing: A Review Journal, vol. 56, pp. 100-109, September 2016.

X. Ning and I. W. Selesnick, “ECG enhancement and QRS detection based on sparse derivatives,” Biomedical Signal Processing and Control, vol. 8, no. 6, pp. 713-723, July 2013.

S. Farashi, “A multiresolution time-dependent entropy method for QRS complex detection,” Biomedical Signal Processing and Control, vol. 24, pp. 63-71, February 2016.

D. Castells-Rufas and J. Carrabina, “Simple real-time QRS detector with the MaMeMi filter,” Biomedical Signal Processing and Control, vol. 21, pp. 137-145, August 2015.

M. R. Homaeinezhad, M. ErfanianMoshiri-Nejad, and H. Naseri, “A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles,” Computers in Biology and Medicine, vol. 44, pp. 66-75, January 2014.

L. D. Sharma and R. K. Sunkaria, “A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency,” Measurement: Journal of the International Measurement Confederation, vol. 87, pp. 194-204, June 2016.

R. J. Martis, U. R. Acharya, and L. C. Min, “ECG beat classification using PCA, LDA, ICA and discrete wavelet transform,” Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437-448, September 2013.

A. Karimipour and M. R. Homaeinezhad, “Real-time electrocardiogram P-QRS-T detection-delineation algorithm based on quality-supported analysis of characteristic templates,” Computers in Biology and Medicine, vol. 52, pp. 153-165, September 2014.

R. Rani, V. S. Chouhan, and H. P. Sinha, “Automated detection of QRS complex in ECG signal using wavelet transform,” International Journal of Computer Science and Network Security, vol. 15, no. 1, pp. 1-5, January 2015.

Z. Zidelmal, A. Amirou, M. Adnane, and A. Belouchrani, “QRS detection based on wavelet coefficients,” Computer Methods and Programs in Biomedicine, vol. 107, no. 3, pp. 490-496, September 2012.

S. Banerjee, R. Gupta, and M. Mitra, “Delineation of ECG characteristic features using multiresolution wavelet analysis method,” Measurement: Journal of the International Measurement Confederation, vol. 45, no. 3, pp. 474-487, April 2012.

A. Kumar and M. Singh, “Optimal selection of wavelet function and decomposition level for removal of ECG signal artifacts,” Journal of Medical Imaging and Health Informatics, vol. 5, no. 1, pp. 138-146, February 2015.

A. Kumar and M. Singh, “Robust multiresolution wavelet analysis and window search based approach for electrocardiogram features delineation,” Journal of Medical Imaging and Health Informatics, vol. 6, no. 1, pp. 146-156, February 2016.

O. El B ’Charri, R. Latif, K. Elmansouri, A. Abenaou, and W. Jenkal, “ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform,” Biomedical Engineering OnLine, vol. 16, no. 26, pp. 1-18, February 2017.

O. El B’Charri, R. Latif, A. Abenaou, and W. Jenkal, “An efficient wavelet-based feature extraction scheme for electrocardiogram signals,” Proc. The International Conf. Wireless Technologies, Embedded and Intelligent Systems, April 2017, pp. 1-4.

A. Grossmann and J. Morlet, “Decomposition of Hardy functions into square integrable wavelets of constant shape,” SIAM Journal on Mathematical Analysis, vol. 15, no. 4, pp. 723-736, 1984.

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, July 1989.

M. Vetterli and C. Herley, “Wavelets and filter banks: theory and design,” IEEE Transactions on Signal Processing, vol. 40, no. 9, pp. 2207-2232, September 1992.

I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123-151, November 2005.

N. G. Kingsbury, “The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters,” Proc. the 8th IEEE DSP Workshop,vol. 8, August 1998, p. 86.

E. Castillo, D. P. Morales, A. García, F. Martínez-Martí, L. Parrilla, and A. J. Palma, “Noise suppression in ECG signals through efficient one-step wavelet processing techniques,” Journal of Applied Mathematics, vol. 2013, pp. 763903-1-763903-13, May 2013.

S. Liu, Y. Li, X. Hu, L. Liu, and D. Hao, “A novel thresholding method in removing noises of electrocardiogram based on wavelet transform,” Journal of Information and Computational Science, vol. 10, no. 15, pp. 5031-5041, October 2013.

D. L. Donoho, “De-Noising by Soft-Thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, May 1995.

G. B. Moody and R. G. Mark, “The MIT-BIH arrhythmia database on CD-ROM and software for use with it,” Proc. Computers in Cardiology, IEEE Press, August 2002, pp. 185-188.

A. Demski and M. L. Soria, “ecg-kit: a matlab toolbox for cardiovascular signal processing,” Journal of Open Research Software, vol. 4, no. 1, pp. 1-4, April 2016.

J. P. V. Madeiro, P. C. Cortez, J. A. L. Marques, C. R. V. Seisdedos, and C. R. M. R. Sobrinho, “An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms,” Medical Engineering & Physics, vol. 34, no. 9, pp. 1236-1246, December 2012.

M. Yochum, C. Renaud, and S. Jacquir, “Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT,” Biomedical Signal Processing and Control, vol. 25, pp. 46-52, November 2016.

C. Li, C. Zheng, and C. Tai, “Detection of ECG characteristic points using wavelet transforms,” IEEE Transactions on biomedical Engineering, vol. 42, no. 1, pp. 21-28, January 1995.

Published
2018-09-28
How to Cite
EL B’charri, O., Latif, R., Abenaou, A., Elmansouri, K., & Jenkal, W. (2018). A Reliable QRS Detection Method Based on Dual-Tree Wavelet Transform. International Journal of Engineering and Technology Innovation, 8(4), 307-320. Retrieved from http://ojs.imeti.org/index.php/IJETI/article/view/1089
Section
Articles