False alarm suppression in early prediction of cardiac arrhythmia

High false alarm rates in intensive care units (ICUs) cause desensitization among care providers, thus risking patients´ lives. Providing early detection of true and false cardiac arrhythmia alarms can alert hospital personnel and avoid alarm fatigue, so that they can act only on true life-threatening alarms, hence improving efficiency in ICUs. However, suppressing false alarms cannot be an excuse to suppress true alarm detection rates. In this study, we investigate a cost-sensitive approach for false alarm suppression while keeping near perfect true alarm detection rates. Our experiments on two life threatening cardiac arrhythmia datasets from Physionet´s MIMIC II repository provide evidence that the proposed method is capable of identifying patterns that can distinguish false and true alarms using on average 60% of the available time series´ length. Using temporal uncertainty estimates of time series predictions, we were able to estimate the confidence in our early classification predictions, therefore providing a cost-sensitive prediction model for ECG signal classification. The results from the proposed method are interpretable, providing medical personnel a visual verification of the predicted results. In conducted experiments, moderate false alarm suppression rates were achieved (34.29% for Asystole and 20.32% for Ventricular Tachycardia) while keeping near 100% true alarm detection, outperforming the state-of-the-art methods, which compromise true alarm detection rate for higher false alarm suppression rate, on these challenging applications.