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Artificial intelligence identifies, locates seizures in real-time

This gif was recorded during two seizures, one at 2950 seconds, the other at 9200. The top left animation is of EEG signals from three electrodes. The top right is a map of the inferred network. The third animation plots the Fiedler eigenvalue, the single value used to detect seizures using the network inference technique. Credit: Li Lab

Researchers from Washington University in St. Louis’ McKelvey School of Engineering have combined artificial intelligence with systems theory to develop a more efficient way to detect and accurately identify an epileptic seizure in real-time.

Their results were published May 26 in the journal Scientific Reports.

The research comes from the lab of Jr-Shin Li, professor in the Preston M. Green Department of Electrical & Systems Engineering, and was headed by Walter Bomela, a postdoctoral fellow in Li’s lab.

Also on the research team were Shuo Wang, a former student of Li’s and now assistant professor at the University of Texas at Arlington, and Chu-An Chou of Northeastern University.

“Our technique allows us to get , process it and extract a feature that’s more informative for the to use,” Bomela said. “The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with much less computing resources.”

In brain science, the current understanding of most seizures is that they occur when normal brain activity is interrupted by a strong, sudden hyper-synchronized firing of a cluster of neurons. During a , if a person is hooked up to an electroencephalograph—a device known as an EEG that measures —the abnormal brain activity is presented as amplified spike-and-wave discharges.

“But the seizure detection accuracy is not that good when temporal EEG signals are used,” Bomela said. The

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