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  • Title: Suppression of false arrhythmia alarms in the ICU: a machine learning approach.
    Author: Ansari S, Belle A, Ghanbari H, Salamango M, Najarian K.
    Journal: Physiol Meas; 2016 Aug; 37(8):1186-203. PubMed ID: 27454017.
    Abstract:
    This 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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