Robust filters for intensive care monitoring: beyond the running median

Current alarm systems in intensive care units create a very high rate of false positive alarms because most of them simply compare physiological measurements to fixed thresholds. An improvement can be expected when the actual measurements are replaced by smoothed estimates of the underlying signal. However, classical filtering procedures are not appropriate for signal extraction, as standard assumptions, such as stationarity, do no hold here: the time series measured often show long periods without change, but also upward or downward trends, sudden shifts and numerous large measurement artefacts. Alternative approaches are needed to extract the relevant information from the data, i.e., the underlying signal of the monitored variables and the relevant patterns of change, such as abrupt shifts and trends. This article reviews recent research on filter-based online signal extraction methods designed for application in intensive care.