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

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. RESULTS: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. CONCLUSIONS: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea

“…Furthermore, the absence of direct contact with the skin allows for the monitoring of pre-term infants and other patients who suffer from skin irritations due to prolonged use of electrodes [7]. Another advantage of using cameras is the direct recognition of motion artifacts that reduce the vital sign quality and trigger false alarms [8].…”

Experiences and mediating factors in nurses' responses to electronic device alarms: A phenomenological study

“…This need to reduce clinically irrelevant alarms should be addressed in collaboration with key stakeholders (e.g., patients and engineers) and health services, taking advantage of the development of technology with better algorithms that reduce false alarms (Bollepalli et al, 2021; Hyland et al, 2020; Ruppel, De Vaux, et al, 2018; Wilken et al, 2019). In addition, the devices must be useful, adjusting the sensitivity and specificity of the alarms, and their operation must be easy to learn (Fernandes et al, 2020; Muroi et al, 2020). Nurse managers must acquire modern devices and facilitate the training and expertise of nurses in the use of those devices.…”

Multichannel esophageal signals to monitor respiratory rate in preterm infants

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