Online signal extraction by robust linear regression
In intensive care, time series of vital parameters have to be analysed online, i.e. without any time delay, since there may be serious consequences for the patient otherwise. Such time series show trends, slope changes and sudden level shifts, and they are overlaid by strong noise and many measurement artefacts. The development of update algorithms and the resulting increase in computational speed allows to apply robust regression techniques to moving time windows for online signal extraction. By simulations and applications we compare the performance of least median of squares, least trimmed squares, repeated median and deepest regression for online signal extraction.
KeywordsRobust filtering least median of squares least trimmed squares repeated median deepest regression breakdown point
Unable to display preview. Download preview PDF.
- Bernholt, T. (2004). Exact Algorithms for the Repeated Median, LMS, LTS and Deepest Regression, Personal Communication.Google Scholar
- Bernholt, T., Fried, R., Gather, U. and Wegener I. (2004). Modified Repeated Median Filters, Technical Report 46, SFB 475, University of Dortmund, Germany.Google Scholar
- Rousseeuw, P.J. (1983). Multivariate Estimation with High Breakdown Point, in W. Grossmann, G. Pflug, I. Vincze, W. Wertz (eds.) Proceedings of the 4th Pannonian Symposium on Mathematical Statistics and Probability, Vol. B, D. Reidel Publishing Company, Dordrecht (The Netherlands).Google Scholar
- Van Kreveld, M., Mitchell, J.S.B., Rousseeuw, P.J., Sharir, M., Snoeyink, J. and Speckmann, B. (1999). Efficient Algorithms for Maximum Regression Depth, Proceedings of the 15th Annual ACM Symposium of Computational Geometry, ACM Press, New York (NJ), 31–40.Google Scholar