Suppression of intensive care unit false alarms based on the arterial blood pressure signal
Journal
IEEE Access
Date Issued
2017-03-31
Author(s)
Koceski, Saso
Abstract
Patient monitoring in intensive care units requires collection and processing of high volumes
of data. High sensitivity of sensors leads to significant number of false alarms, which cause alarm fatigue. Reduction of false alarms can lead to better reaction time of medical personnel. This paper aims to develop a method for false alarm suppression and evaluate it on a publicly available data set with manually annotated alarms. First, an automated feature engineering was performed using the signal for arterial blood pressure (ABP) and a processed signal that contained the times of each heartbeat from the ABP signal. Next, support vector machines, random forest, and extreme random trees classifiers were trained to create classification models. The best suppression performance was achieved for the extreme tachycardia alarm, for which 90.3% of the false alarms were suppressed, while only 0.54% of the true alarms were incorrectly suppressed. This paper demonstrates that alarm suppression can be achieved with high accuracy using an automated feature engineering coupled with machine learning algorithms. The proposed approach can be
utilized as aid to medical personnel and experts, allowing them to be more productive and to respond to alarms in a more timely manner.
of data. High sensitivity of sensors leads to significant number of false alarms, which cause alarm fatigue. Reduction of false alarms can lead to better reaction time of medical personnel. This paper aims to develop a method for false alarm suppression and evaluate it on a publicly available data set with manually annotated alarms. First, an automated feature engineering was performed using the signal for arterial blood pressure (ABP) and a processed signal that contained the times of each heartbeat from the ABP signal. Next, support vector machines, random forest, and extreme random trees classifiers were trained to create classification models. The best suppression performance was achieved for the extreme tachycardia alarm, for which 90.3% of the false alarms were suppressed, while only 0.54% of the true alarms were incorrectly suppressed. This paper demonstrates that alarm suppression can be achieved with high accuracy using an automated feature engineering coupled with machine learning algorithms. The proposed approach can be
utilized as aid to medical personnel and experts, allowing them to be more productive and to respond to alarms in a more timely manner.
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