Advertisement

Annals of Biomedical Engineering

, Volume 15, Issue 1, pp 79–89 | Cite as

A Monte Carlo technique for signal level detection in implanted intracranial pressure monitoring

  • Randy K. Avent
  • John D. Charlton
  • H. Troy Nagle
  • Richard N. Johnson
Article

Abstract

Statistical monitoring techniques like CUSUM, Trigg's tracking signal and EMP filtering have a major advantage over more recent techniques, such as Kalman filtering, because of their inherent simplicity. In many biomedical applications, such as electronic implantable devices, these simpler techniques have greater utility because of the reduced requirements on power, logic complexity and sampling speed. The determination of signal means using some of the earlier techniques are reviewed in this paper, and a new Monte Carlo based method with greater capability to sparsely sample a waveform and obtain an accurate mean value is presented. This technique may find widespread use as a trend detection method when reduced power consumption is a requirement.

Keywords

Trend detection Monte Carlo method Time series analysis ICP sampling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allen, R. Time series methods in the monitoring of intracranial pressure. Part I: problems, suggestions for a monitoring scheme and review of appropriate techniques.J. Biomed. Eng. 5:5–18, 1983.PubMedGoogle Scholar
  2. 2.
    Harrison, P.J. and O.L. Davies. The use of cumulative sum (CUSUM) techniques for the control of routine forecasts of product demand.Operations Res. 12:325–333, 1963.Google Scholar
  3. 3.
    Hitchings, D.J., M.J. Campbell and D.E.M. Taylor. Trend detection of pseudo-random variables using an exponentially mapped past statistical approach: An adjunct to computer assisted monitoring.Int. J. Bio-Medical Computing 6:73–87, 1975.Google Scholar
  4. 4.
    Kahn, H.Symposium on Monte Carlo Methods. New York: Wiley, 1956.Google Scholar
  5. 5.
    Lewis, C.D. Statistical monitoring techniques.Med. and Biol. Eng., 9:315–323, 1971.Google Scholar
  6. 6.
    Martin, F.F.Computer Modeling and Simulation. New York: Wiley, 1968.Google Scholar
  7. 7.
    Marmarou, A., K. Shulman and R.M. Rosende. A nonlinear analysis of the cerebrospinal fluid system and intracranial pressure dynamics.J. Neurosurg. 48:332–344, 1978.PubMedGoogle Scholar
  8. 8.
    Nyquist, H. Certain topics in telegraph transmission theory.Trans. AIEE 47:617–644, 1928.Google Scholar
  9. 9.
    Organick, E.I. and L.P. Meissner.Fortran IV. Reading, MA: Addison-Wesley, 1974.Google Scholar
  10. 10.
    Szweczykowski, J., J. Korsak-Sliwka, A. Kunicki, S. Sliwka, J. Dziduszka and P. Dytoko.Intracranial Pressure IV. New York: Springer Verlag 1980, 419–422.Google Scholar
  11. 11.
    Trigg, D.W. Monitoring a forecasting system.Opl. Res. Q. 15:271–274, 1964.Google Scholar

Copyright information

© Pergamon Journals Ltd 1987

Authors and Affiliations

  • Randy K. Avent
    • 1
    • 2
  • John D. Charlton
    • 1
    • 2
  • H. Troy Nagle
    • 1
    • 2
  • Richard N. Johnson
    • 1
    • 2
  1. 1.Biomedical Engineering CurriculumUniversity of North CarolinaChapel Hill
  2. 2.Department of Electrical and Computer EngineeringNorth Carolina State UniversityRaleigh

Personalised recommendations