Lameski, Petre and Zdravevski, Eftim and Koceski, Saso and Kulakov, Andrea and Trajkovik, Vladimir (2017) Suppression of Intensive Care Unit False Alarms Based on the Arterial Blood Pressure Signal. IEEE Access, 5. pp. 5829-5836.
Preview |
Text
article_final.pdf Download (4MB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Subjects: | Natural sciences > Computer and information sciences Engineering and Technology > Electrical engineering, electronic engineering, information engineering |
| Divisions: | Faculty of Computer Science |
| Depositing User: | Saso Koceski |
| Date Deposited: | 18 Jan 2018 09:22 |
| Last Modified: | 18 Jan 2018 09:22 |
| URI: | https://eprints.ugd.edu.mk/id/eprint/18762 |
Actions (login required)
 |
View Item |