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.