Small-computer procedure for optimal filtering of haemodynamic data

  • L. Jetto


Most reported methods dealing with the optimal filtering of haemodynamic data are based on computationally onerous procedures and pay little attention to execution speed. In the paper the problem of filtering noisy aortic flow and pressure waveforms is considered and the possibility of applying a Kalman filter technique, which does not require a prèliminary identification of the signal generation process, is discussed. The basic hypothesis is that, in a cardiac cycle, the waveforms considered can be described by functions which permit Taylor series expansion. Thus the signal model is easy to obtain and the filtering procedure is fast, even if implemented on a minicomputer. The filter was applied to flow and pressure signals simultaneously measured in the canine ascending aorta under different physiological conditions. The filter performance is described and discussed.


Aortic flow signals Kalman filter Noisy digital filtering Pressure signals 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bartoli, F., Baselli, G. andCerutti, S. (1982) Application of identification and linear filtering algorithms to the R-R interval measurements.Computers in cardiology, IEEE Conference, Seattle.Google Scholar
  2. Bartoli, F. andCerutti, S. (1982) A Kalman filter procedure for the processing of the electroencephalogram. IEEE ICASSP 82, Paris, 721–724.Google Scholar
  3. Benson, D. W. Jr. (1971) An algorithm for defining the cardiac cycle using ascending aortic blood flow.Comput. Biomed. Res.,4, 216–223.CrossRefGoogle Scholar
  4. Broman, H. (1973) On line digital filtering of noisy records.Res. Lab. Med. Electr. (Goteborg),4.Google Scholar
  5. Broman, H., Kvanicka, J., Liander, B. andVarnavskas, E. (1975) A computerized system for optimal filtering of left ventricular pressure.IEEE Trans.,BME-4, 287–292.Google Scholar
  6. Burattini, R. andGnudi, G. (1981) On the identificability of an arterial tree input impedance model. Proceedings of the Fourth National Congress of Theoretical and Applied Mechanics. Varna, Bulgaria,2, 179–186.Google Scholar
  7. Burattini, R. andGnudi, G. (1982) Computer identification of models for the arterial tree input impedance: Comparison between two new simple models and first experimental results.Med. & Biol. Eng. & Comput.,20, 134–144.Google Scholar
  8. Burattini, R., Gnudi, G., Westerhof, N. andFioretti, S. (1983) Total systemic arterial compliance and aortic characteristic impedance in the dog as a function of pressure: a model based study. Int. Rep.n.7/83, Dipartimento di Electtronica e Automatica, Universitá di Ancona.Google Scholar
  9. Burattini, R., Fioretti, S. andJetto, L. (1984) A simple algorithm for defining the mean cardiac cycle of aortic flow and pressure during steady-state. Proc. of the Sixth Nordic Meeting of the Biological Engineering Society, July 1984, Aberdeen.Google Scholar
  10. Deswysen, B., Charlier, A. A. andGevers, M. (1980) Quantitative evaluation of the systemic arterial bed by parameter estimation of a simple model.Med. & Biol. Eng. & Comput.,18, 153–166.CrossRefGoogle Scholar
  11. Eykhoff, P. (1974)System identification. John Wiley & Sons, London, 410–415.Google Scholar
  12. Fozzard, H. A., Kinias, P. andPai, A. L. (1974) Algorithms for analysis of on-line pressure signals.Computers in cardiology, IEEE Conference, Seattle, 77–80.Google Scholar
  13. Gelb, A. (1982)Applied optimal estimation. MIT Press, 277–315.Google Scholar
  14. Jazwinski, A. H. (1970)Stochastic processes and filtering theory. Academic Press, New York, 269–271.MATHGoogle Scholar
  15. Kinias, P., Fozzard, H. A. andNorusis, M. J. (1981) A real time pressure algorithm.Comput. Bio. Med.,11, 211–220.Google Scholar
  16. Laxminarayan, S., Sipkema, P. andWesterhof, W. (1978) Characterization of the arterial system in the time domain.IEEE Trans.,BME-25, 177–184.Google Scholar
  17. Lynn, P. A. (1977) Online digital filters for biological signals: some fast designs for a small computer.Med. & Biol. Eng. & Comput. 15, 534–540.CrossRefGoogle Scholar
  18. Maybeck, P. S. (1979)Stochastic models, estimation and control, vol. 1. Academic Press, New York, 206–226.MATHGoogle Scholar
  19. Mihram, G. A. (1972)Simulation: statistical foundations and methology. Academic Press, New York, 437–477.Google Scholar
  20. Murgo, J. P., Giolma, J. P. andAltobelli, S. A. (1977) Physiologic signal acquisition and processing for human haemodynamic research in a clinical cardiac-catheterization laboratory.Proc. IEEE,65, 696–702.Google Scholar
  21. Stockman, G., Kanal, L. andKyle, M. C. (1976) Structural pattern recognition of carotid pulse waves using a general waveform parising system.Comms Ass. Comput. Mach.,19, 688–695.MATHGoogle Scholar
  22. Swanson, G. D. (1977) Biological signal conditioning for system identification.Proc. IEEE,65, 735–740.CrossRefGoogle Scholar
  23. Westerhof, N., Elzinga, G. andSipkema, P. (1971) An artificial arterial system for pumping hearts.J. Appl. Physiol.,31, 776.Google Scholar
  24. Yoganathan, A. P., Gupta, R. andCorcoran, W. H. (1976) Fast Fourier transform in the analysis of biomedical data.Med. & Biol. Eng.,14, 239–244.Google Scholar

Copyright information

© IFMBE 1985

Authors and Affiliations

  • L. Jetto
    • 1
  1. 1.Department of Electronics and AutomaticaUniversity of AnconaAnconaItaly

Personalised recommendations