Skip to main content
SpringerLink
Log in

Online Digital Filter and QRS Detector Applicable in Low Resource ECG Monitoring Systems

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The present work describes fast computation methods for real-time digital filtration and QRS detection, both applicable in autonomous personal ECG systems for long-term monitoring. Since such devices work under considerable artifacts of intensive body and electrode movements, the input filtering should provide high-quality ECG signals supporting the accurate ECG interpretation. In this respect, we propose a combined high-pass and power-line interference rejection filter, introducing the simple principle of averaging of samples with a predefined distance between them. In our implementation (sampling frequency of 250 Hz), we applied averaging over 17 samples distanced by 10 samples (Filter10x17), thus realizing a comb filter with a zero at 50 Hz and high-pass cut-off at 1.1 Hz. Filter10x17 affords very fast filtering procedure at the price of minimal computing resources. Another benefit concerns the small ECG distortions introduced by the filter, providing its powerful application in the preprocessing module of diagnostic systems analyzing the ECG morphology. Filter10x17 does not attenuate the QRS amplitude, or introduce significant ST-segment elevation/depression. The filter output produces a constant error, leading to uniform shifting of the entire P-QRS-T segment toward about 5% of the R-peak amplitude. Tests with standardized ECG signals proved that Filter10x17 is capable to remove very strong baseline wanderings, and to fully suppress 50 Hz interferences. By changing the number of the averaged samples and the distance between them, a filter design with different cut-off and zero frequency could be easily achieved. The real-time QRS detector is designed with simplified computations over single channel, low-resolution ECGs. It relies on simple evaluations of amplitudes and slopes, including history of their mean values estimated over the preceding beats, smart adjustable thresholds, as well as linear logical rules for identification of the R-peaks in real-time. The performance of the QRS detector was tested with internationally recognized ECG databases (AHA, MIT-BIH, European ST-T database), showing mean sensitivity of 99.65% and positive predictive value of 99.57%. The performance of the presented QRS detector can be highly rated, comparable and even better than other published real-time QRS detectors. Examples representing some typical unfavorable conditions in real ECGs, illustrate the common operation of Filter10x17 and the QRS detector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Afonso V., W. Tompkins, T. Nguyen, S. Luo. ECG beat detection using filter banks IEEE Trans. Biomed. Eng. 46, 192–202, 1999. doi:10.1109/10.740882.

    Article  PubMed  CAS  Google Scholar 

  2. American Heart Association (AHA) ventricular arrhythmia ECG database. Emergency Care Research Institute 5200 Butler Pike, Plymouth Meeting, PA 19462, USA, 1984.

  3. ANSI/AAMI ECE57. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. (AAMI) Recommended Practice/American National Standard, 1998.

  4. Bai, Y., W. Chu, Ch. Chen, Y. Lee, Y. Tsai, and Ch. Tsai. The combination of Kaiser window and moving average for the low-pass filtering of the remote ECG signals. In: Proc. 17th IEEE Symposium on Computer-Based Medical Systems, 273, 2004.

  5. Christov, I. Real time electrocardiogram QRS detection using combined adaptive threshold. BioMed. Eng. OnLine 3:28, 2004. http://www.biomedical-engineering-online.com/content/3/1/28. doi:10.1186/1475-925X-3-28.

  6. Christov I., I. Dotsinsky, I. Daskalov. High-pass filtering of ECG signals using QRS elimination. Med. & Biol. Eng & Comp. 30, 253–256, 1992. doi:10.1007/BF02446141.

    Article  CAS  Google Scholar 

  7. Dotsinsky I., T. Stoyanov. Optimization of bi-directional digital filtering for drift suppression in electrocardiogram signals. J. Med. Eng. & Technol. 28, 178–180, 2004. doi:10.1080/03091900410001675996.

    Article  PubMed  CAS  Google Scholar 

  8. Dotsinsky, I., and T. Stoyanov. Ventricular beat detection in single channel electrocardiograms. BioMed. Eng. OnLine 3:3, 2004. http://www.biomedical-engineering-online.com/content/3/1/3. doi:10.1186/1475-925X-3-3.

  9. European Society of Cardiology ST-T Database, CNR Institute of Clinical Physiology, Computer Laboratory, via Trieste, 41 56100 Pisa, Italy. http://physionet.org/physiobank/database/edb/.

  10. Faes Th., H. Govaerts, B. Tenvoorde, O. Rompelman. Frequency synthesis of digital filters based on repeatedly applied unweighed moving average operations, Med. & Biol. Eng & Comp. 32, 698–701, 1994. doi:10.1007/BF02524254.

    Article  CAS  Google Scholar 

  11. IEC 62D/60601-2-27. Particular requirements for the safety of electrocardiographic monitoring equipment (equivalent to AAMI EC 13), 1994.

  12. Iliev I., V. Krasteva, S. Tabakov. Real-time detection of pathological cardiac events in the electrocardiogram. Physiol. Meas. 28, 259–276, 2007. doi:10.1088/0967-3334/28/3/003.

    Article  PubMed  Google Scholar 

  13. Jane R., P. Laguna, N. Thakor, P. Caminal. Adaptive Baseline Wander Removal in the ECG: Comparative Analysis with Cubic Spline Technique. IEEE Comp. Cardiol. 19, 143–146, 1992. doi:10.1109/CIC.1992.269426.

    Article  Google Scholar 

  14. Kligfield P., L. Gettes, J. Bailey, R. Childers, B. Deal, W. Hancock, G. Herpen, J. Kors, P. Macfarlane, D. Mirvis, O. Pahlm, P. Rautaharju, G. Wagner. Recommendations for the standardization and interpretation of the electrocardiogram, Part I. The electrocardiogram and its technology. J. Am.Coll. Cardiol. 49, 1109–1127, 2007. doi:10.1016/j.jacc.2007.01.024.

    Article  PubMed  Google Scholar 

  15. Kunzmann U., G. von Wagner, J. Schochlin, A. Bolz. Parameter extraction of ECG signals in real-time. Biomed. Tech. 47, 875–8, 2002.

    Article  Google Scholar 

  16. Lee J., K. Kim, B. Lee, M. Lee A real time QRS detection using delay-coordinate mapping for the microcontroller implementation. Ann. Biomed. Eng. 30, 1140–1151, 2002. doi:10.1114/1.1523030.

    Article  PubMed  Google Scholar 

  17. Łęskia J., N. Henzel. ECG baseline wander and powerline interference reduction using nonlinear filter bank. Signal Processing 85, 781–793, 2005. doi:10.1016/j.sigpro.2004.12.001.

    Article  Google Scholar 

  18. Levkov, Ch., G. Mihov, R. Ivanov, I. Daskalov, I. Christov, and I. Dotsinsky. Removal of power-line interference from the ECG: a review of the subtraction procedure, BioMed. Eng. OnLine 4:50, 2005. http://www.biomedical-engineering-online.com/content/4/1/50. doi:10.1186/1475-925X-4-50.

    Google Scholar 

  19. Lynn P. Online digital filters for biological signals: some fast designs for a small computer. Med. & Biol. Eng & Comp. 15, 534–40, 1977. doi:10.1007/BF02442281.

    Article  CAS  Google Scholar 

  20. Ma W.K., Y.T. Zhang, F.S. Yang. A fast recursive-least-squares adaptive notch filter and it is applications to biomedical signals. Med. & Biol. Eng & Comp. 37, 99–103, 1999. doi:10.1007/BF02513273.

    Article  CAS  Google Scholar 

  21. MIT-BIH Arrhythmia Database, http://physionet.ph.biu.ac.il/physiobank/database/mitdb.

  22. Mneimneh M., E. Yaz, M. Johnson, R. Povinelli. An Adaptive Kalman Filter for Removing Baseline Wandering in ECG Signals. IEEE Comp. Cardiol. 33, 253–256, 2006.

    Google Scholar 

  23. Paoletti M., C. Marchesi. Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis. Comp. Methods Programs Biomed. 82, 20–30, 2006. doi:10.1016/j.cmpb.2006.01.005.

    Article  Google Scholar 

  24. Pei S., C. Tseng. Elimination of AC Interference in Electrocardiogram Using IIR Notch Filter with Transient Suppression. IEEE Trans. Biomed. Eng. 42, 1128–1132, 1995. doi:10.1109/10.469385.

    Article  PubMed  CAS  Google Scholar 

  25. Pipberger H., R. Arzbaecher, A. Berson, et al. Recommendations for standardization of leads and of specifications for instruments in electrocardiography and vectorcardiography: report of the Committee on Electrocardiography. Circulation 52, 11–31, 1975.

    Google Scholar 

  26. Ruha A., S. Sallinen, S. Nissila. A real-time microprocessor QRS detector system with a 1-ms timing accuracy for the measurement of ambulatory HRV. IEEE Trans. Biomed. Eng. 44, 159–67, 1997. doi:10.1109/10.554762.

    Article  PubMed  CAS  Google Scholar 

  27. Shusterman V., S. Shah, A. Beigel, K. Anderson. Enhancing the Precision of ECG Baseline Correction: Selective Filtering and Removal of Residual Error. Comp. Biomed. Research 33, 144–160, 2000. doi:10.1006/cbmr.2000.1539.

    Article  CAS  Google Scholar 

  28. Suppappola S., Y. Sun. Nonlinear transforms of ECG signals for digital QRS detection: a quantitative analysis. IEEE Trans. Biomed. Eng. 41, 397–400, 1994. doi:10.1109/10.284971.

    Article  PubMed  CAS  Google Scholar 

  29. Tinati, M., and B. Mozaffary. Wavelet packets approach to electrocardiograph baseline drift cancellation, Int. J. Biomed. Imag. Article ID 97157:1–9, 2006.

    Google Scholar 

  30. Thakor N., Y. Zhu. Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38, 785–794, 1991 doi:10.1109/10.83591.

    Article  PubMed  CAS  Google Scholar 

  31. Xu L., D. Zhang, K. Wang. Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms. IEEE Trans. Biomed. Eng. 52, 1973–1975, 2005. doi:10.1109/TBME.2005.856296.

    Article  PubMed  Google Scholar 

  32. Zywietz, Chr. CTS-ECG Test Atlas. Hannover: Center for Computer Electrocardiography, Biosignal Processing, Medical School, 1999.

Download references

Acknowledgment

This work has been supported by the National Science Fund Grant (BY-TH-101)/2005 of the Bulgarian Ministry of Education and Science.

Author information

Authors and Affiliations

  1. Department of Electronics, Technical University of Sofia, 8 Kl. Ohridski str., 1000, Sofia, Bulgaria

    Serafim Tabakov & Ivo Iliev

  2. Centre of Biomedical Engineering ‘Prof. Ivan Daskalov’, Bulgarian Academy of Sciences, Acad. G. Bonchev str., Bl.105, 1113, Sofia, Bulgaria

    Vessela Krasteva

Authors
  1. Serafim Tabakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivo Iliev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vessela Krasteva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vessela Krasteva.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tabakov, S., Iliev, I. & Krasteva, V. Online Digital Filter and QRS Detector Applicable in Low Resource ECG Monitoring Systems. Ann Biomed Eng 36, 1805–1815 (2008). https://doi.org/10.1007/s10439-008-9553-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-008-9553-5

Keywords

Search

Navigation