Computational and Mathematical Methods in Medicine
Volume 2011 (2011), Article ID 143480, 11 pages
Reducing False Alarms of Intensive Care Online-Monitoring Systems: An Evaluation of Two Signal Extraction Algorithms
1Fakultät Statistik, Technische Universität Dortmund, 44227 Dortmund, Germany
2Universitätsklinikum Regensburg, 93042 Regensburg, Germany
3Helios Klinikum Berlin-Buch, 13125 Berlin, Germany
4Abteilung für Medizinische Informatik, Biometrie und Epidemiologie, Ruhr-Universität, Bochum, 44801 Bochum, Germany
Received 16 August 2010; Accepted 11 January 2011
Academic Editor: Yvonne Vergouwe
Copyright © 2011 M. Borowski et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.