Reducing false arrhythmia alarms using robust interval estimation and machine learning

Reducing false arrhythmia alarms in the intensive care unit is the objective of the PhysioNet/Computing in Cardiology Challenge 2015. In this paper, an approach is presented that analyzes multimodal cardiac signals in terms of their beat-to-beat intervals as well as their average rhythmicity. Based on this analysis, several features in time and frequency domain are extracted and used for subsequent machine learning. Results show that alarm-specific strategies proved optimal for different types of arrhythmia and that obtained scores varied: While the score for reducing false ventricular tachycardia alarms was 68:91, false extreme tachycardia alarms could be suppressed with perfect accuracy. Overall, a top score of 75.55 / 75.18 could be achieved for real-time / retrospective false alarm reduction.