Patient monitoring generates a large number of alarms, the vast majority of which are false. Excessive non-actionable medical alarms leads to alarm fatigue, a well-recognized patient safety issue 1 . Previous neonatal research in our group has identified specific patterns in red alarms (critical alarms) that can be exploited to reduce clinically non-actionable alarms 2 . Exploratory research on the early detection of red alarms based on the occurrence of yellow alarms (alerts) has also shown promise for early detection but is limited by poor sensitivity and specificity.
The aim of this project is to develop an algorithm for early warning of alarms using the continuously acquired vital signs, the heart rate, SpO2 and breathing rate, for the early detection of red desaturation and red bradycardia alarms in preterm infants.
The project will be based on developing features and using a machine learning approach for classifying those yellow alarms that lead to a red alarm within a short window of time. Data for the project is available and has been acquired from the data warehouse in the NICU of Máxima Medical Center, Veldhoven.
- A master’s student in Electrical Engineering, Biomedical Engineering, Computer Science or related discipline.
- Good background in signal processing and machine learning (or willingness to learn).
- Good Matlab programming skills.
- Willingness to learn neonatal physiology and develop physiologically meaningful features.
This project is offered by Philips Research, Eindhoven (HTC). The project will however be carried out at the department of clinica physics at Máxima Medical Center, Veldhoven which is where the student will be located.