Detection of False Alarms in the NICU Using Pressure Sensitive Mat

Abstract

In the neonatal intensive care unit (NICU), a large proportion of alarms are false. This can result in alarm fatigue which increases the risk that alarms of clinical significance are overlooked and may lead to an increased response time. It is therefore of interest to minimize false alarms in the NICU to reduce alarm fatigue. Previous alarm classification systems rely on physiologic data and waveforms. In this study, we explore the use of a pressure sensitive mat (PSM), which is an unobtrusive and non-contact secondary sensor system that captures motion-related data. We use a dataset of 136 manually annotated alarm events for 10 neonatal subjects to train a machine learning model for the detection of false alarms. Results show that a combination of physiologic and PSM features has the best performance, which achieves a 0.87 macro-averaged F1 score, compared to the model that solely relies on physiologic data which only achieves a 0.73 macro-averaged F1 score. We also show that the use of PSM data improves the model's ability to generalize to unseen patients using a leave-one-subject-out test protocol. This study demonstrates that the PSM provides complementary and useful information for Improving the discrimination of true and false alarms.