Time-varying digital filtering of ECG baseline wander

  • L. Sörnmo
Computing and Data Processing

Abstract

Time-varying filtering techniques are applied to the problem of baseline correction by letting the cut-off frequency of a linear filter be controlled by the low-frequency properties of the ECG signal. The time-varying filter is implemented as a bank of linear low-pass filters, in which each filter has a slightly differing cut-off frequency. Sampling rate decimation and interpolation are employed because the design of a filter for baseline reduction can be treated as a narrowband filtering problem. All filters have a linear phase response to reduce, for example, ST-segment distortion. The performance of the technique presented is studied on ECG signals with different types of simulated baseline wander. The results are compared with the performance of time-invariant linear filtering and cubic spline interpolation. The results show that an improvement in performance can be achieved when using time-varying filtering, especially at low heart rates or during episodes with excessive baseline wander.

Keywords

Baseline wander Cutoff frequency ECG Exercise Filtering Noise Stress test Time-varying 

References

  1. AHA (1990) Recommendations for standardization and specifications in automated electrocardiography: bandwidth and digital signal processing. American Heart Association Committee on Electrocardiography.Circ.,81, 730–739.Google Scholar
  2. Bartoli, F., Cerutti, S. andGatti, E. (1983) Digital filtering and regression algorithms for an accurate detection of the baseline in ECG signals.Med. Inform.,8, 71–82.CrossRefGoogle Scholar
  3. De Sà J. P. M. (1982) Digital FIR filtering for removal of ECG baseline wander.J. Clin. Eng.,7, 235–239.Google Scholar
  4. Froning, J., Froelicher, V. F. andOlson, M. D. (1987) Application and limitations of continuous baseline estimation and removal using cubic-spline technique during exercise ECG testing. Proc. Computers in Cardiology, Conf., IEEE Comput. Soc., 537–540.Google Scholar
  5. Froning, J., Olson, M. D. andFroelicher, V. F. (1988) Problems and limitations of ECG baseline estimation and removal using cubic spline technique during exercise ECG testing: recommendations for proper implementation.J. Electrocardiol., Suppl.,21, 149–157.CrossRefGoogle Scholar
  6. Gradwohl, J. R., Pottala, E. W., Horton, M. R. andBailey, J. J. (1988) Comparison of two methods for removing baseline wander in the ECG. Proc. Computers in Cardiology Conf., IEEE Comput. Soc., 493–496.Google Scholar
  7. Jarske, P., Nuevo, Y. andMitra, S. K. (1988) A simple approach to the designof linear phase FIR digital filters with variable characteristics.Sig. Proc.,14, 313–326.CrossRefGoogle Scholar
  8. Longini, R. L., Giolma, J. P., Wall, C. andQuick, R. F. (1975) Filtering without phase shift.IEEE Trans.,BME-22, 432–433.Google Scholar
  9. MacFarlane, P. W., Peden, J. Lennox, G., Watts, M. P. andLawrie, T. D. V. (1977) The Glasgow system. InProceedings of trends in computer-processed electrocardiograms.Van Bemmel, J. H., andWillems, J. L. (Eds.), North-Holland, 143–150.Google Scholar
  10. Mäkivirta, A., Estola, K.-P., Saramäki, T., Kalli, S. andJärvinen, K. (1985) Exercise ECG preprocessor with a high-performance digital filter. Proc. 14th Int. Conf. Med. & Biol. Eng., Espoo, Finland, 11th–16th Aug., 629–630.Google Scholar
  11. Mäkivirta, A., Estola, K.-P. andKalli, S. (1986) Digital quality enhancement of analog ECG signal. Proc. Computers in Cardiology Conf., IEEE Comput. Soc., 699–702.Google Scholar
  12. McClellan, J. H., Parks, T. W. andRabiner, R. L. (1973) A computer program for designing FIR optimum linear phase digital filters.IEEE Trans.,AU-21, 506–526.Google Scholar
  13. Meyer, C. R. andKeiser, H. N. (1977) Electrocardiogram baseline noise estimation and removal using cubic splines and state space computation.Comput. Biomed. Res.,10, 450–470.CrossRefGoogle Scholar
  14. Mortara, D. (1978) Digital filters for ECG signals. Proc. Computers in Cardiology Conf., IEEE Comput. Soc., 511–514.Google Scholar
  15. Nygårds, M.-E. andSörnmo, L. (1983) Delineation of the QRS complex using the envelope of the e.c.g..Med. & Biol. Eng. & Comput.,21, 538–547.CrossRefGoogle Scholar
  16. Pahlm, O. andSörnmo, L. (1987) Data processing of exercise ECGs.IEEE Trans.,BME-34, 158–165.Google Scholar
  17. Riedl, H., Ertel, W., Hoffman, H. andHott, K. H. (1977) The Siemens program. InProceedings of trends in computer-processed electrocardiograms.Van Bemmel, J. H., andWillems, J. L. (Eds.) North-Holland, 191–196.Google Scholar
  18. Talmon, J. L. (1983) Pattern recognition of the ECG. Thesis, Free University, AmsterdamGoogle Scholar
  19. van Alsté, J. A. andSchilder, T. S. (1985) Removal baseline wander and powerline interference from the ECG by an efficient filter with reduced number of taps.IEEE Trans.,BME-32, 1052–1061.Google Scholar
  20. van Alsté, J., van Eck, W. andHerrman, O. E. (1986) ECG baseline wander reduction using linear phase filters.Comput. Biomed. Res.,19, 417–427.CrossRefGoogle Scholar
  21. Yi-Sheng, Z. andThakor, N. V. (1986) P-wave detection by an adaptive QRS-T cancellation technique. Proc. Computers in Cardiology Conf., IEEE Comput. Soc., 249–252.Google Scholar

Copyright information

© IFMBE 1993

Authors and Affiliations

  • L. Sörnmo
    • 1
    • 2
  1. 1.Department of Clinical PhysiologyLund UniversityLundSweden
  2. 2.Department of Signal ProcessingLund UniversityLundSweden

Personalised recommendations