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.
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 raw data, process it and extract a feature that’s more informative for the machine learning model 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.”
