Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection

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Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection

Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection

Carl Muroi, Sando Meier, Valeria De Luca, David J Mack, Christian Strässle, Patrick Schwab, Walter Karlen, Emanuela Keller

Abstract

Background: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos.

Methods: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices.

November 15, 2023
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