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Physiological measurement · Aug 2016
Suppression of false arrhythmia alarms in the ICU: a machine learning approach.
- Sardar Ansari, Ashwin Belle, Hamid Ghanbari, Mark Salamango, and Kayvan Najarian.
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA. Michigan Center for Integrative Research in Clinical Care, University of Michigan, Ann Arbor, MI, USA.
- Physiol Meas. 2016 Aug 1; 37 (8): 1186-203.
AbstractThis paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors. The outputs of the beat detection algorithms are combined using a machine learning approach. For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.
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