Duke Researchers to Monitor Brain Injury with Machine Learning

10/27/20 Pratt School of Engineering

Multi-year scientific research agreement from CortiCare to engineer a machine-learning approach to continuously monitor patients with brain injuries

A brain made of blue light with streaks of blue light coming out of it and matrix digits in the background
Duke Researchers to Monitor Brain Injury with Machine Learning

Neurologists and electrical engineers at Duke University are teaming up in an ambitious effort to develop a better way to monitor brain health for all patients in the ICU.  Dubbed “Neurologic Injury Monitoring and Real-time Output Display,” the method will use machine learning and continuous electroencephalogram (EEG) data along with other clinical information to assist providers with assessment of brain injury and brain health.

Current practices for monitoring brain-injured patients include regular clinical exam assessments made every few hours around the clock. However, many patients are not able to follow commands or participate in the physical exam, so doctors can only examine gross responses to loud noises, pinches and noxious stimulation as well as rudimentary brain stem reflexes.

“Not only are these exams often limited in their scope, imaging only provides a snapshot of the brain at the time the images are taken,” said Brad Kolls, associate professor of neurology at Duke University School of Medicine and principal investigator on the new research study.

The new approach will leverage continuous brainwave activity along with other clinical information from the medical record and standard bedside monitoring to allow a more comprehensive assessment of the state of the brain. Kolls and Leslie Collins, professor of electrical and computer engineering at Duke, hope to improve the care of brain-injured patients by correlating this data with outcomes. This will allow clinicians to optimize brain function and personalize recovery.

With extensive experience in combining machine learning applications with biological signals, Collins will use unsupervised learning such as topic modeling and automated feature extraction to delve into the novel dataset.

“We have promising results from using this approach to analyze data taken from sleeping patients,” said Collins. “We’re excited to be able to change the care, and potentially the outcomes, of patients with brain injury.”

The program is sponsored by CortiCare Inc., a leading provider of electroencephalography services to hospitals in the U.S. and internationally. CortiCare has funded this multi-year research agreement supporting the program and intends to commercialize the work once completed. The program is expected to run until the fall of 2022.