Machine learning aids in detection of "brain tsunamis," University of Cincinnati study finds

Notice: News releases are not subject to review by MedLink Neurology’s Editorial Board.

A University of Cincinnati study found machine learning models can aid in the automation and detection of abnormal brain activity sometimes referred to as a “brain tsunami.”

UC’s Jed Hartings PhD, is corresponding author of the study published March 12 in the journal Scientific Reports detailing how automation can aid clinicians treating patients with spreading depolarizations.

What is a spreading depolarization?

Hartings said spreading depolarizationss are believed to occur in patients with virtually any type of acute brain injury, including different kinds of strokes and traumatic brain injuries. Approximately 60% to 100% of all patients in these different disease categories are believed to experience spreading depolarization.

Just like a battery, brain cells have a stored, or polarized, charge that enables them to send signals to one another. During spreading depolarization, brain cells become depolarized and unable to send these electrical signals, which Hartings said essentially turns brain cells into a “big bag of saltwater that’s not functional anymore.”

“This happens en masse in a local area of tissue and then spreads out like a wave, like ripples in a pond, and it interrupts every aspect of cell function,” said Hartings, professor and vice chair of research in the Department of Neurosurgery in UC’s College of Medicine.

Spreading depolarizations can occur continuously in patients for several days, but they can also continue on and off up to two weeks after a severe brain injury.

Study details and results

Research has focused on patients who have had an electrode strip surgically placed in the brain to monitor for spreading depolarizations. Physicians also need to receive specific training on reading the brain wave recordings so they can constantly monitor the data.

“This is time-intensive and expensive, and few physicians have the highly specialized expertise to diagnose spreading depolarizations reliably,” Hartings said. “Therefore, we wanted to automate spreading depolarization diagnosis to make this monitoring more accessible and widely available.”

Hartings and his colleagues used more than 2,000 hours of brain monitoring data from 24 patients who were hospitalized for severe traumatic brain injury, and experts manually reviewed and identified more than 3,500 unique spreading depolarization events in the data set.

Half of this patient data was used to train a machine learning model how to accurately recognize and classify spreading depolarization events. Once the model was trained, researchers used the other half of data to see how accurately it could identify spreading depolarizations in “new” data it hadn’t seen before.

“We showed that the method is able to identify spreading depolarizations with a high degree of sensitivity and specificity,” Hartings said. “Overall, the performance was similar to an expert human scorer.”

Unexpectedly, the team found the algorithm could detect many spreading depolarization events that were not identified using human scoring, likely due to a higher degree of objectivity. Testing the limits of the algorithm, researchers found it could achieve a high degree of performance using one voltage reading per 10 seconds, compared to the typical method of collecting 256 data points per second.

“If we could achieve a high degree of performance with minimal information, this would leave a lot of ‘headroom’ to improve performance even further by adding in the additional information,” Hartings said. “That work is ongoing now.”

Study impact

Hartings said when the algorithm is fully realized and implemented, automated spreading depolarization detection would allow any neurosurgical center to monitor patients for spreading depolarizations even if they do not have a physician on staff with this specialized training.

“Many neurosurgical centers are interested in monitoring spreading depolarizations for patient care but simply don’t have the knowledge or resources to implement it,” he said. “Having automated spreading depolarization reading will lower these barriers and hopefully make this technology more accessible, and thus accelerate research and hopefully patient benefit.”

While the study results are promising, Hartings cautioned there is further development and validation needed before automated detection fully replaces human expertise and detection of spreading depolarizations.

“I think we are headed in that direction. But even if not, automated detection would, at a minimum, considerably reduce workload and increase response times, since alarms could alert physicians to review data or take action earlier than they might otherwise following usual intervals to review patient progress,” he said. “This is another significant benefit that should not be overlooked.”

Limitations of the study include the need for the electrode strip to be placed on the brain during neurosurgery, limiting the number of patients who can be monitored to those undergoing surgery. However, Hartings said research is ongoing to develop noninvasive detection methods that could be used to monitor a larger population of patients.

Moving forward, Hartings and his colleagues are continuing to refine the algorithm using larger data sets and testing software implementation, with the plan for other institutions to trial the software and to begin using it for patient care and research.

Additionally, the team is conducting clinical trials like the INDICT trial to determine optimal treatments for spreading depolarizations. Having a more precise detection method coupled with better tools to treat spreading depolarizations could have a significant impact on patient care.

Source: News Release
University of Cincinnati
March 19, 2025