AI detects early warning signs of epilepsy without visible seizures

Epilepsy isn't always easy to diagnose. Seizures often don't occur during routine brain-wave recordings (EEGs), leaving doctors without the direct observation they need to make a clear diagnosis. University of Delaware researchers and collaborators are working to close that gap, using artificial intelligence to detect early warning signs hidden in the brain's electrical rhythms.

In a proof-of-concept study in mice, the team showed that their approach can identify subtle EEG differences linked to a genetic form of epilepsy, even when no visible seizures occurred. The findings, published in the Journal of Neural Engineering, set the stage for the next phase of the research, which will test the method on EEGs from children being evaluated for epilepsy at Nemours Children's Health.

A dictionary of brain waves

Neurologists often use EEGs to help diagnose epilepsy, but routine recordings offer only about a 20-minute snapshot of brain activity. Without a seizure captured during that window, clinicians must look for far subtler clues that can be difficult to detect visually.

That's where AI comes in. The UD researchers' algorithm works much like a language learner encountering an unfamiliar tongue. It starts by identifying patterns that appear frequently in EEG recordings and learns what they mean in context, effectively building a dictionary of electrical patterns.

Our machine-learning approach lets the algorithm learn the brain's 'language' of waveforms, spotting subtle patterns humans might miss during manual review."

Austin Brockmeier, assistant professor in electrical and computer engineering and computer and information sciences

Starting small with a mouse model

When Brockmeier, a faculty mentor in UD's interdisciplinary neuroscience graduate (ING) program, presented his computational neuroscience research at an ING seminar, he caught the attention of Amanda Hernan, an affiliated associate professor of psychological and brain sciences and biomedical engineering at UD and senior research scientist at Nemours Children's Health. Hernan, who is also an ING faculty mentor, studies how variations in brain activity affect thinking and learning in children with epilepsy.

The two decided to put machine learning to the test using EEGs from mice with epilepsy-causing variations in the TSC1 gene. The researchers used a panel of more than 40 mice, including animals with and without the gene variation, across three different genetic backgrounds, or strains. They extracted EEG segments from five days of recordings from each mouse for analysis.

Because the EEG segments contained no seizure activity, the algorithm had to detect differences in the brain's baseline activity alone. It was able to distinguish between the mouse strains and to detect the TSC1 gene variation with high accuracy in two of the three strains.

"These results show that EEG patterns contain measurable signals of neurological differences, even without visible seizures," Hernan said.

Taking it to the clinic

Now, the team is taking their method out of the lab and into the clinic. With funding from the Delaware Clinical and Translational Research ACCEL Program, Brockmeier and Hernan will next apply their approach to EEG recordings from children being evaluated for epilepsy at Nemours Children's Health.

Pediatric EEGs are shorter than the multi-day recordings used in the mouse study, and children present with many different types of epilepsy. But the researchers are optimistic.

"The goal is to identify biomarkers that flag underlying changes in the brain's electrical activity before seizures occur," Hernan said. Earlier detection could lead to earlier treatment and less uncertainty for families.

That uncertainty, Hernan said, takes a toll. "Seizures follow natural cycles, but without a way to know where you are in that cycle, the anticipation can be incredibly anxiety-provoking," she explained.

Better pattern recognition could also improve treatment decisions. For example, if a new medication is introduced during a natural lull in seizure activity, its benefits could be overestimated.

Looking further ahead, the researchers envision a future where wearable EEG devices allow continuous, real-time monitoring for those with high risk of seizures. Similar approaches could eventually be applied to other neurological conditions, including autism and ADHD.

"This is a step toward precision medicine," Brockmeier said. "Brain-wave typing could help identify which interventions will work best for a given patient."

For families navigating the daily uncertainty of epilepsy, that kind of precision could make a huge difference.