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Stepwise Detection of the QRS Complex in the ECG Signal

심전도 신호에서 QRS군의 단계적 검출

  • Kim, Jeong-Hong (Kyungpook National University. School of Computer Science and Engineering) ;
  • Lee, SeungMin (Kyungpook National University Graduate School of Electronics Engineering) ;
  • Park, Kil-Houm (Kyungpook National University Graduate School of Electronics Engineering)
  • Received : 2015.08.17
  • Accepted : 2015.11.28
  • Published : 2016.02.29

Abstract

The QRS complex of ECG signal represents the depolarization and repolarization activities in the cells of ventricle. Accurate informations of $QRS_{onset}$ and $QRS_{offset}$ 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 $R_{peak}$, 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 $QRS_{onset}$ and $QRS_{offset}$. 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.

ECG 신호에서 QRS군은 매우 중요한 심실의 탈분극 상태 정보를 제공한다. 자동으로 심전도 신호를 분석하기 위해서는 $QRS_{onset}$$QRS_{offset}$ 에 대한 정확한 정보가 중요하다. 본 연구에서는 먼저 QRS군의 전위값 변화량 및 $R_{peak}$ 와의 거리를 이용하여 Q파와 R파의 접속 부분 그리고 R파와 S파의 접속 부분을 구하였다. 다음 단계에서는 이를 기준으로 적분연산을 이용하여 $QRS_{onset}$$QRS_{offset}$ 을 검출하였다. 알고리즘의 성능을 평가하기위해 PhysioNet QT database를 사용하여 심장 전문의가 수작업으로 표시한 결과에 대한 평균과 표준편차를 계산하였다. 실험결과에서 제안한 알고리즘의 표준편차는 전문의사가 수용할 수 있는 허용치 범위 안에 속하며, 다른 알고리즘보다 더 우수함을 나타낸다.

Keywords

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. https://doi.org/10.1080/03091900512331332587
  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. https://doi.org/10.7840/kics.2015.40.4.731
  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. https://doi.org/10.7840/KICS.2012.37C.4.313
  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. https://doi.org/10.1109/TIM.2013.2279001
  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. https://doi.org/10.1109/TBME.2006.884644
  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. https://doi.org/10.1088/0967-3334/36/8/1729
  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. https://doi.org/10.1007/BF02442378
  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. https://doi.org/10.1109/TBME.2003.821031
  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. https://doi.org/10.1109/TBME.2003.821031
  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. https://doi.org/10.1109/TBME.2010.2068048
  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. https://doi.org/10.9708/jksci.2013.18.8.131
  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. https://doi.org/10.1006/cbmr.1994.1006
  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. https://doi.org/10.1109/10.16474
  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. https://doi.org/10.1016/j.cmpb.2008.04.006
  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.

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