title: Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms.
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.
authors: Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany. borowski@statistik.tu-dortmund.de
authors: M
authors: M
authors: Borowski
authors: S
authors: S
authors: Siebig
authors: C
authors: C
authors: Wrede
authors: M
authors: M
authors: Imhoff
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001794:Blood Pressure / Q000502:physiology; D056902:Clinical Alarms; D003422:Critical Care / Q000379:methods*; D004868:Equipment Failure; D005189:False Positive Reactions; D005858:Germany; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D009862:Online Systems / Q000295:instrumentation*; D010100:Oxygen / Q000097:blood; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
chemical_list: D010100:Oxygen