A new noninvasive method can effectively map the source and scale of seizure activity in people with epilepsy, according to a recent study. The tool could lower the number of surgeries needed to treat epilepsy. Clinicians often rely on an invasive technique to try to find the area responsible for someone’s seizures by implanting electrodes deep into the the outer layer of the brain, in one surgery before removing brain tissue in a second surgery. But these methods can lead to bleeding and infection.
The new approach couples machine learning, a type of artificial intelligence (AI), with electroencephalography (EEG) recordings from 76 electrodes placed on the scalp, to localize seizure activity in the brain. Unlike previous EEG techniques, it can also show how much brain tissue is involved. However, noninvasive imaging techniques come with trade-offs. While EEG is adept at recording activity during a seizure, it cannot always home in on a seizure’s source with precision. The layers of fluid, skull and brain tissue that lie between the electrodes and the underlying brain activity can distort the signal. And reverse-engineering the source location from that distorted signal is mathematically difficult.
Previous EEG methods could “pinpoint the center of gravity” of the recorded seizure activity, says lead investigator Bin He, professor of biomedical engineering at Carnegie Mellon University in Pittsburgh, Pennsylvania. But knowing where seizure activity is centered is not enough for surgical treatment of epilepsy.
To circumvent this problem, Bin Hee and his colleagues developed a new algorithm, called “fast spatio-temporal iteratively reweighted edge sparsity (FAST-IRES)”. Rather than just localizing the source, their algorithm provides information about the extent of the brain network that gives rise to the EEG signal. FAST-IRES also uses machine learning to calculate the thresholds at which signals are deemed significant. As a proof of concept, Hee and his colleagues tested FAST-IRES on EEG recordings from 36 people with epilepsy who were pursuing surgery as treatment. The researchers found that their method was as successful as implanted electrodes at finding the location and extent of the seizure source and would have produced similar surgical outcomes.