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. 2011:2011:143480.
doi: 10.1155/2011/143480. Epub 2011 Feb 27.

Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms

M Borowski  1 , S Siebig, C Wrede, M Imhoff
Affiliations

Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms

M Borowski et al. Comput Math Methods Med. 2011.

Abstract

Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.

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Figures

Figure 1
Figure 1
Generated time series data (dotted) consisting of the true signal (solid) overlaid with noise and outliers. Although the signal is within the alarm bounds (dashed), outliers would cause several unnecessary threshold alarms.
Figure 2
Figure 2
The aoRM algorithm.
Figure 3
Figure 3
(a) aoRM signal estimations (solid) overshoot after sudden changes in the data (dotted). (b) effect of the restrict-to-range rule (5).
Figure 4
Figure 4
Measurements of mean arterial blood pressure (ART.M, dotted) and RM signal estimations (solid).
Figure 5
Figure 5
SE and FARR of aoRM and aoTRM-LS for each of the four vital signs. The number below (above) a dot (cross) indicates the used n min.
See this image and copyright information in PMC

References

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