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 Chun-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.”
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 seizure, if a person is hooked up to an electroencephalograph — a device known as an EEG that measures electrical output — 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 team developed a network inference technique to facilitate detection of a seizure and pinpoint its location with improved accuracy.
During an EEG session, a person has electrodes attached to different spots on his/her head, each recording electrical activity around that spot.