• Rebeca Salas-Boni, Yong Bai, Patricia Rae Eileen Harris, Barbara J Drew, and Xiao Hu.
• Department of Physiological Nursing, University of California, San Francisco, CA, USA. Electronic address: rebeca.salas-boni@ucsf.edu.
• J Electrocardiol. 2014 Nov 1;47(6):775-80.

AbstractOver the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in false negative alarms. Our approach is based on features extracted from the ECG signal 20 seconds prior to a triggered alarm. We applied a multi resolution wavelet transform to the ECG data 20seconds prior to the alarm trigger, extracted features from appropriately chosen scales and combined them across all available leads. These representations are presented to a L1-regularized logistic regression classifier. Results are shown in two datasets of physiological waveforms with manually assessed cardiac monitor alarms: the MIMIC II dataset, where we achieved a false alarm (FA) suppression of 21% with zero true alarm (TA) suppression; and a dataset compiled by UCSF and General Electric, where a 36% FA suppression was achieved with a zero TA suppression. The methodology described in this work could be implemented to reduce the number of false monitor alarms in other arrhythmias.Copyright © 2014 Elsevier Inc. All rights reserved.

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