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BioMed Research International
Volume 2017, Article ID 5168346, 12 pages
https://doi.org/10.1155/2017/5168346
Research Article

A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform

1School of Information Science and Engineering, Central South University, Changsha, Hunan Province 410083, China
2Hunan Vocational College of Commerce, Changsha, Hunan Province 410205, China
3School of Computer Science and Educational Software, Guangzhou University, Guangzhou, Guangdong Province 510006, China

Correspondence should be addressed to Guojun Wang; nc.ude.usc@gnawjgsc

Received 16 December 2016; Revised 18 February 2017; Accepted 2 April 2017; Published 17 May 2017

Academic Editor: Volker Rasche

Copyright © 2017 Ziran Peng and Guojun Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. A. Fedotov and S. A. Akulov, Mathematical Modeling and Analysis of Errors of Measuring Transducers of Biomedical Signals, Fizmatlit, Moscow, Russia, 2013 (Russian).
  2. Z. Peng, G. Wang, H. Jiang, and S. Meng, “Research and improvement of ECG compression algorithm based on EZW,” Computer Methods and Programs in Biomedicine, vol. 145, pp. 157–166, 2017. View at Publisher · View at Google Scholar
  3. S. Lahmiri and M. Boukadoum, “A weighted bio-signal denoising approach using empirical mode decomposition,” Biomedical Engineering Letters, vol. 5, no. 2, pp. 131–139, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 1, pp. 85–98, 1990. View at Publisher · View at Google Scholar
  5. W. J. Tompkins, Ed., Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC, Prentice Hall, Upper Saddle River, NJ, USA, 1993.
  6. F. J. Theis and A. Meyer-Bäse, Biomedical Signal Analysis: Contemporary Methods and Applications, MIT Press, 2010.
  7. M. Elgendi, M. Jonkman, and F. DeBoer, “Frequency bands effects on QRS detection,” in Proceedings of the 3rd International Conference on Bio-Inspired Systems and Signal Processing, pp. 428–431, Valencia, Spain, January 2010. View at Publisher · View at Google Scholar
  8. T. Kato, S. Hirose, S. Kumagai, A. Ozaki, S. Matsumoto, and M. Inoko, “Electrocardiography as the first step for the further examination of cardiac involvement in myasthenia gravis,” BioMed Research International, vol. 2016, Article ID 8058946, 4 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Benitez, P. Gaydecki, A. Zaidi, and A. Fitzpatrick, “The use of the Hilbert transform in ECG signal analysis,” Computers in Biology and Medicine, vol. 31, no. 5, pp. 399–406, 2001. View at Publisher · View at Google Scholar
  10. R. M. Rangayyan, Biomedical Signal Analysis, Fizmatlit, Moscow, Russia, 2007 (Russian).
  11. G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001. View at Publisher · View at Google Scholar
  12. J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230–236, 1985. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Ruha, S. Sallinen, and S. Nissila, “A real-time microprocessor QRS detector system with a 1-ms timing accuracy for the measurement of ambulatory HRV,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 3, pp. 159–167, 1997. View at Publisher · View at Google Scholar
  14. S. Kadambe, R. Murray, and G. Paye Boudreaux-Bartels, “Wavelet transform-based QRS complex detector,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 7, pp. 838–848, 1999. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Abboud, O. Berenfeld, and D. Sadeh, “Simulation of high-resolution QRS complex using a ventricular model with a fractal conduction system. Effects of ischemia on high-frequency QRS potentials,” Circulation Research, vol. 68, no. 6, pp. 1751–1760, 1991. View at Publisher · View at Google Scholar · View at Scopus
  16. O. Berenfeld, D. Sadeh, and S. Abboud, “Simulation of late potentials using a computerized three dimensional model of the heart's ventricles with fractal conduction system,” in Proceedings of the Computers in Cardiology, p. 137, 1989.
  17. O. Berenfeld, D. Sadeh, and S. Abboud, “Modeling of the heart's ventricular conduction system using fractal geometry: spectral analysis of the QRS complex,” Annals of Biomedical Engineering, vol. 21, no. 2, pp. 125–134, 1993. View at Publisher · View at Google Scholar · View at Scopus
  18. J. P. Martínez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, “A wavelet-based ECG delineator: evaluation on standard databases,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, pp. 570–581, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Gahi, M. Lamrani, A. Zoglat, M. Guennoun, B. Kapralos, and K. El-Khatib, “Biometric identification system based on electrocardiogram data,” in Proceedings of the New Technologies, Mobility and Security, pp. 1–5, Tangier, Morocco, 2008.
  20. M. S. Manikandan and S. Dandapat, “Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression,” Biomedical Signal Processing and Control, vol. 3, no. 1, pp. 44–66, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. B.-U. Köhler, C. Hennig, and R. Orglmeister, “The principles of software QRS detection,” IEEE Engineering in Medicine and Biology Magazine, vol. 21, no. 1, pp. 42–57, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. O. Pahlm and L. Sörnmo, “Software QRS detection in ambulatory monitoring—a review,” Medical & Biological Engineering & Computing, vol. 22, no. 4, pp. 289–297, 1984. View at Publisher · View at Google Scholar · View at Scopus
  23. F. Lucena, A. K. Barros, and N. Ohnishi, “The performance of short-term heart rate variability in the detection of congestive heart failure,” BioMed Research International, vol. 2016, Article ID 1675785, 11 pages, 2016. View at Publisher · View at Google Scholar