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Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 expert-labeled life-threatening arrhythmias, from 182 separate ICU visits. On the independent test data, FA suppression results with no true alarm (TA) suppression were 86.4% for asystole, 100% for extreme bradycardia and 27.8% for extreme tachycardia. For the ventricular tachycardia alarms, the best FA suppression performance was 30.5% with a TA suppression rate below 1%. To reduce the TA suppression rate to zero, a reduction in FA suppression performance to 19.7% was required.

Original publication

DOI

10.1016/j.jelectrocard.2012.07.015

Type

Journal article

Journal

J Electrocardiol

Publication Date

11/2012

Volume

45

Pages

596 - 603

Keywords

Arrhythmias, Cardiac, Artificial Intelligence, Clinical Alarms, Critical Care, Diagnosis, Computer-Assisted, Diagnostic Errors, False Positive Reactions, Humans, Monitoring, Physiologic, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity