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QRS detection of ECG signal using hybrid derivative and MaMeMi filter by effectively eliminating the baseline wander

  • Anu Sheetal
  • Harjit SinghEmail author
  • Anureet Kaur
Article
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Abstract

Presently in medical science, diagnostic process demands longer electrocardiogram (ECG) signal recordings, which have traditionally been processed on high-speed multicore personal computers. However, for local ECG signal collection and processing, a highly efficient, low-power battery-driven portable devices are essential for efficient and convenient clinical use. For this, an accurate and improved QRS detector is required which can effectively eliminate the baseline wander. In this paper, a novel technique to detect the QRS complex of ECG signal with hybrid filter composed of derivative and maximum mean minimum (MaMeMi) filter has been proposed. The performance of this technique is compared with the existing technique that consists of MaMeMi filter only for the QRS detection. Various parameters such as sensitivity, specificity, accuracy, detection error rate and elapsed time are used for analyzing the results on the MIT-BIH Arrhythmia database. It is observed that overall performance of the proposed technique is better than the existing technique for these parameters.

Keywords

ECG QRS detection Baseline wander MaMeMi filter Derivative filter 

References

  1. 1.
    Sadhukhana, D., & Mitra, M. (2012). R-peak detection algorithm for ECG using double difference and RR interval processing. Procedia Technology, 4, 873–877.CrossRefGoogle Scholar
  2. 2.
    Zhang, H. (2012). An improved QRS wave group detection algorithm and matlab implementation. International Conference on Solid State Devices and Materials Science, 25, 1010–1016.Google Scholar
  3. 3.
    Alvarez, R. A., Penin, A. J. M., & Sobrino, X. A. V. (2013). A comparison of three QRS detection algorithms over a public database. Conference on Enterprise Information Systems, 9, 1159–1165.Google Scholar
  4. 4.
    Rufas, D. C., & Carrabina, J. (2015). Simple real-time QRS detector with the MaMeMi filter. Biomedical Signal Processing and Control, 21, 137–145.CrossRefGoogle Scholar
  5. 5.
    Moody, G. B., & Mark, R. G. (2001). The impact of MIT/BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 45, 739–5175.Google Scholar
  6. 6.
    Arefin, M. R., & Fazel-Rezai, R. (2014). Computationally efficient QRS detection analysis based on dual-slope method. In 36th Annual international conference of IEEE engineering in medicine and biology society (pp. 2274–2277).Google Scholar
  7. 7.
    Ravanshad, N., Rezaee-Dehsorkh, H., Lotfi, R., & Lian, Y. (2014). A level-crossing based QRS-detection algorithm for wearable ECG sensors. EEE Journal of Biomedical and Health Informatics, 18, 183–192.CrossRefGoogle Scholar
  8. 8.
    Hamilton, P. S., & Tompkins, W. J. (1986). Quantitative investigation of QRS detection rules using MIT/BIH arrhythmia database. IEEE Transactions on Biomedical Engineering, 33(12), 1157–1165.CrossRefGoogle Scholar
  9. 9.
    Agrawal, S., & Gupta, A. (2013). Projection operator based removal of baseline wander noise from ECG signals. In Asilomar conference on signals, systems and computers (pp. 957–961).Google Scholar
  10. 10.
    Sufi, F., Fang, Q., & Cosic, I. (2007). ECG R-R peak detection on mobile phones. In 29th Annual international conference of IEEE engineering in medicine and biology society (pp. 465–475).Google Scholar
  11. 11.
    Zhang, F., & Lian, Y. (2009). QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks. IEEE Transactions on Biomedical Circuits and Systems, 3(1), 220–228.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Verma, A. R., & Singh Y. (2015). Adaptive tuneable notch filter for ECG signal enhancement. In 3rd International conference on recent trends in computing (Vol. 57, pp 332–337).Google Scholar
  13. 13.
    Chen, S.-W., Chen, H.-C., & Chan, H.-L. (2006). A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. IEEE Transactions on Biomedical Engineering, 82(3), 187–195.Google Scholar
  14. 14.
    Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transform. IEEE Transactions on Biomedical Engineering, 42(1), 21–28.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.GNDUGurdaspurIndia

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