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

Borowski M, Siebig S, Wrede C, Imhoff M - Comput Math Methods Med (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany. borowski@statistik.tu-dortmund.de

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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Related in: MedlinePlus

(a) aoRM signal estimations (solid) overshoot after sudden changes in the data (dotted). (b) effect of the restrict-to-range rule (5).
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fig3: (a) aoRM signal estimations (solid) overshoot after sudden changes in the data (dotted). (b) effect of the restrict-to-range rule (5).

Mentions: The aoRM filter estimates the signal at the rightmost or current time point, meaning that the signal is extracted without relevant time delay. (Its computing time depends on the chosen input parameters. The R package robfilter [17] provides a function of the aoRM; see Section 5.2. We applied this aoRM function using a 2.3 GHz computer with 2 GB RAM and obtained a mean computing time of 0.007 seconds for one iteration, resp., time point.) However, estimating the signal by the level at the right end of the regression line implies that the signal estimates possibly deviate distinctly from the data, especially when level shifts occur. Then, signal estimates “overshoot”; that is, they leave the range given by the window observations, see Figure 3(a). It shows a generated time series (dotted) with upwards and downwards level shifts at time points t = 50 and t = 100 and the corresponding aoRM signal estimation time series (solid). Around time t = 70 and t = 120, aoRM signal estimations overshoot. Those overshoots are crucial in our context since a sudden change in the data may cause the signal estimations to cross an alarm limit although the measurements do not, as can be seen in the figure. That is, an aoRM-based alarm system could theoretically cause more false alarms than a system based on raw measurements.


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

Borowski M, Siebig S, Wrede C, Imhoff M - Comput Math Methods Med (2011)

(a) aoRM signal estimations (solid) overshoot after sudden changes in the data (dotted). (b) effect of the restrict-to-range rule (5).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3064994&req=5

fig3: (a) aoRM signal estimations (solid) overshoot after sudden changes in the data (dotted). (b) effect of the restrict-to-range rule (5).
Mentions: The aoRM filter estimates the signal at the rightmost or current time point, meaning that the signal is extracted without relevant time delay. (Its computing time depends on the chosen input parameters. The R package robfilter [17] provides a function of the aoRM; see Section 5.2. We applied this aoRM function using a 2.3 GHz computer with 2 GB RAM and obtained a mean computing time of 0.007 seconds for one iteration, resp., time point.) However, estimating the signal by the level at the right end of the regression line implies that the signal estimates possibly deviate distinctly from the data, especially when level shifts occur. Then, signal estimates “overshoot”; that is, they leave the range given by the window observations, see Figure 3(a). It shows a generated time series (dotted) with upwards and downwards level shifts at time points t = 50 and t = 100 and the corresponding aoRM signal estimation time series (solid). Around time t = 70 and t = 120, aoRM signal estimations overshoot. Those overshoots are crucial in our context since a sudden change in the data may cause the signal estimations to cross an alarm limit although the measurements do not, as can be seen in the figure. That is, an aoRM-based alarm system could theoretically cause more false alarms than a system based on raw measurements.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany. borowski@statistik.tu-dortmund.de

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.

Show MeSH
Related in: MedlinePlus