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Stepwise Detection of the QRS Complex in the ECG Signal
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 Title & Authors
Stepwise Detection of the QRS Complex in the ECG Signal
Kim, Jeong-Hong; Lee, SeungMin; Park, Kil-Houm;
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 Abstract
The QRS complex of ECG signal represents the depolarization and repolarization activities in the cells of ventricle. Accurate informations of and are needed for automatic analysis of ECG waves. In this study, using the amount of change in the QRS complex voltage values and the distance from the , we determined the junction point from Q-wave to R-wave and the junction point from R-wave to S-wave. In the next step, using the integral calculation based on the connection point, we detected and . We use the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and standard deviation of the differences between onsets or offsets manually marked by cardiologists and those detected by the proposed algorithm. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.
 Keywords
ECG;QRS Complex;QRS Detection;QT Database;
 Language
Korean
 Cited by
1.
P-Waves and T-Wave Detection Algorithm in the ECG Signals Using Step-by-Step Baseline Alignment, Journal of Korea Multimedia Society, 2016, 19, 6, 1034  crossref(new windwow)
 References
1.
R. J. Huszar, "Basic dysrhythmias: interpretation & management," Mosby, 2007.

2.
H. L. Chan, W. S. Chou, S. W. Chen, S. C. Fang, C. S. Liou, and Y. S. Hwang, "Continuous and online analysis of heart rate variability," J. Med. Eng. and Technol., vol. 29, no. 5, pp. 227-234, 2005. crossref(new window)

3.
G. D. Clifford, F. Azuaje, and P. McSharry, Advanced methods and tools for ECG data analysis, Artech House, 2006.

4.
B. M. Oussama, B. M. Saadi, and H. S. Zine-Eddine, "Extracting features from ECG and respiratory signals for automatic supervised classification of heartbeat using neural networks," Asian J. Inf. Technol., vol. 15, no. 1, pp. 5-11, 2016.

5.
S. M. Lee, J. S. Kim, and K. H. Park, "PVC detection based on the distortion of QRS complex on ECG signal," J. KICS, vol. 40, no. 4, pp. 731-739, 2015.

6.
J. J. Koo and G. S. Choi, "Performance evaluation of ECG compression algorithms using classification of signals based PQSRT wave features," J. KICS, vol. 37, no. 4, pp. 313-320, 2012.

7.
S. Banerjee and M. Mitra, "Application of cross wavelet transform for ECG pattern analysis and classification," IEEE Trans. Instrumentation and Measurement, vol. 63, no. 2, pp. 326-333, 2014. crossref(new window)

8.
Q. Zhang, A. I. Manriquez, C. Medigue, Y. Papelier, and M. Sorine, "An algorithm for robust and efficient location of T-wave ends in electrocardiograms," IEEE Trans. Biomedical Eng., vol. 53, no. 12, pp. 2544-2552, 2006. crossref(new window)

9.
M. J. Mollakazemi, S. A. Atyabi, and A. Ghaffari, "Heart beat detection using a multimodal data coupling method," Physiological Measurement, vol. 36, pp. 1729-1742, 2015. crossref(new window)

10.
J. Pan, and W. Tompkins, "A real-time QRS detection algorithm," IEEE Trans. Biomedical Eng., vol. 32, no. 3, pp. 230-236, 1985.

11.
M. E. Nygards and L. Sornmo, "Delineation of the QRS complex using the envelope of the ECG," Medical and Biological Eng. Comput., vol. 21, pp. 538-547, 1983. crossref(new window)

12.
J. P. Martinez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, "A wavelet-based ECG delineator: Evaluation on standard databases," IEEE Trans. Biomedical Eng., vol. 51, no. 4, pp. 570-581, 2004. crossref(new window)

13.
A. I. Manriquez and Q. Zhang, "An algorithm for QRS onset and offset detection in single lead electrocardiogram records," in Proc. 29th Annu. Int. Conf. IEEE Eng. in Medicine and Biology Soc., pp. 541-544, Lyon, France, Aug. 2007.

14.
J. P. Martinez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, "A wavelet-based ECG delineator: Evaluation on standard database," IEEE Trans. Biomedical Eng., vol. 51, no. 4, pp. 570-581, 2004. crossref(new window)

15.
P. Laguna, R. G. Mark, A. Goldberg, and G. B. Moody, "A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG," Computers in Cardiology, pp. 673-676, Lund, Sweden, Sept. 1997.

16.
M. Llamedo and J. P. Martinez, "Heartbeat classification using feature selection driven by database generalization criteria," IEEE Trans. Biomedical Eng., vol. 58, no. 3, pp. 616-625, 2011. crossref(new window)

17.
S. O. Kim, "Arrhythmia detection using rhythm features of ECG signal," J. The Korea Soc. of Comput. and Inf., vol. 18, no. 8, pp. 131-139, 2013.

18.
P. Laguna, R. Jane, and P. Caminal, "Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database," Comput. Biomedical Res., vol. 27, no. 1, pp. 45-60, 1994. crossref(new window)

19.
J. Dumont, A. I. Hernandez, and G. Carrault, "Parameter optimization of a wavelet-based electrocardiogram delineator with an evolutionary algorithm," IEEE Computers in Cardiology, pp. 707-710, Lyon, France, Sept. 2005.

20.
C. H. H. Chu and E. J. Delp, "Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators," IEEE Trans. Biomedical Eng., vol. 36, no. 2, pp. 262-273, 1989. crossref(new window)

21.
Y. C. Yeh and W. J. Wang, "QRS complexes detection for ECG signal: the difference operation method," Computer Methods and Programs in Biomedicine, vol. 91, no. 3, pp. 245-254, 2008. crossref(new window)

22.
I. S. Cho and H. S. Kwon, "Advanced R wave detection algorithm using wavelet and adaptive threshold," J. KICS, vol. 35, no. 10, pp. 840-846, 2010.