High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A collection of 750 data segments was made available for training and a set of 500 was held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. In the 2015 PhysioNet/Computing in Cardiology Challenge, we aim to address the problem of high false rrhythmia alarm rates by encouraging the development of new algorithms to improve the specificity of ICU alarms. In this Challenge, we have focused on five types of lifethreatening arrhythmia events, which we have defined as follows: Asystole (ASY): There are no heartbeats at all for a period of four seconds or more. Extreme bradycardia (EBR): The patient's heart rate is lower than 40 beats per minute; fewer than five beats occur within a period of six seconds. Extreme tachycardia (ETC): The heart rate is higher than 140 beats per minute; more than 17 beats occur within a period of 6.85 seconds. Ventricular tachycardia (VTA): There are five or more consecutive ventricular beats within a period of 2.4 seconds (a rate of 100 per minute.) Ventricular fibrillation or flutter (VFB): The heart exhibits a rapid fibrillatory, flutter, or oscillatory waveform for at least four seconds. Participants in the Challenge were given samples of ICU patient waveforms that were identified by the bedside monitor as falling into one of the above categories, and were tasked with devising an algorithm to determine which of these alarms represented true arrhythmias, and which were caused by other factors (such as noise, patient movement, leads falling off, or mis-identification of ECG features on the part of the monitor.) The Challenge was divided into two events. Event 1 was a simulation of the real-time alarm suppression problem: the algorithm needed to determine whether the alarm was true or false based solely on the information available before the alarm was first triggered. In Event 2, algorithms were also able to see 30 seconds' worth of waveform data following the time of the alarm, and could use this information to retrospectively classify the alarm as true or false. The development of an algorithm that could reliably solve either of these problems would be a major step forward in patient care. Computing in Cardiology 2015; 42:273-276.
The PhysioNet 2015 challenge on false alarm reduction in electrocardiography (ECG) monitoring REF was one of the most notable efforts to date to address the issue of false alarms in physiological monitoring.
The PhysioNet/Computing in Cardiology Challenge 2015: Reducing false arrhythmia alarms in the ICU
2015 Computing in Cardiology Conference (CinC)
2015 Computing in Cardiology Conference (CinC)
Medicine Computer Science