Machine learning aids in detection of 'brain tsunamis'

MSN highlighted research led by the University of Cincinnati's Jed Hartings detailing how automation and machine learning can aid clinicians treating patients with spreading depolarizations, sometimes referred to as “brain tsunamis.”

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 (SDs).

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

Just like a battery, brain cells have a stored, or polarized, charge that enables them to send signals to one another. During SD, 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.”

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

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

“We showed that the method is able to identify SDs with a high degree of sensitivity and specificity,” said Hartings, professor and vice chair of research in the Department of Neurosurgery in UC’s College of Medicine. “Overall, the performance was similar to an expert human scorer.”

Read the MSN article.

Featured image at top of neurons. Photo credit/iStockPhoto.