  High false alarm rate in intensive care units (ICUs) has been identified as
one of the most critical medical challenges in recent years. This often results
in overwhelming the clinical staff by numerous false or unurgent alarms and
decreasing the quality of care through enhancing the probability of missing
true alarms as well as causing delirium, stress, sleep deprivation and
depressed immune systems for patients. One major cause of false alarms in
clinical practice is that the collected signals from different devices are
processed individually to trigger an alarm, while there exists a considerable
chance that the signal collected from one device is corrupted by noise or
motion artifacts. In this paper, we propose a low-computational complexity yet
accurate game-theoretic feature selection method which is based on a genetic
algorithm that identifies the most informative biomarkers across the signals
collected from various monitoring devices and can considerably reduce the rate
of false alarms.
