On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units

This paper presents an alarm validation system dedicated to patient monitoring in intensive care units (ICU). Several physiological signals are continuously acquired and an on-line trend extraction method is implemented for each one. A trend is a succession of contiguous semi-quantitative episodes, expressing the time evolution of a signal with several symbols. The difference between the trend and the signal is considered as a residual. In this paper, trend extraction is based on several thresholds that are adapted on-line, following the signal variations. Multivariate change indices are further deduced from the trends and the residuals. They provide an indication of changes to patient hemodynamic and respiratory state. An alarm validation system based on these indices is then proposed, which uses fuzzy decision making. Whenever a monitoring system sets off an alarm, the system proposed carries out a backward analysis of the physiological variables monitored. The system enables various policies to be implemented: filtering of false alarms due to artifacts, confirmation of true alarms due to a patient state change. The system was tested on more than 50 h of data recorded on adult patients in an ICU unit, when 105 SpO(2) alarms were set off by a fixed threshold alarm system. The comparison between the decision made on-line by the validation system and the decision made by a medical expert for each of these alarms showed that the system is able to recognize 100% of true alarms and filter 50-80% of false alarms. Copyright (C) 2009 John Wiley & Sons, Ltd.