Sufficient warnings could afford patients time to prepare for the onset of a seizure by taking medication. Those who don’t respond well enough to anti-epileptic drugs would also have the chance to minimize the risk of seizure-related injuries.

Epiness, according to the BGU researchers, minimizes the number of EEG electrodes that a wearer would need to use. They developed and tested algorithms using EEG data from epilepsy patients who were monitored for several days before surgery. The algorithm with the best prediction performance was 97 percent accurate, and it kept almost the same level of performance (95 percent) with fewer electrodes.

It’ll probably be some time yet before the device is available to epilepsy patients. A new startup called NeuroHelp has licensed the Epiness tech for further development and commercialization. Clinical trials for a prototype are scheduled for later this year.

While it’s not a complete fix, the new AI system, developed by Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette, is a major leap forward from existing prediction methods. Currently, other methods analyze brain activity with an EEG (electroencephalogram) test and apply a predictive model afterwards. The new method does both of those things at once, with the help of a deep learning algorithm that maps brain activity and another that can predict the electrical channels lighting up during a seizure.

It’ll still be some time before this technique will be available for widespread use — the team is now working on a custom chip that can help process the necessary algorithms — but it could be life-changing news for patients with epilepsy.