Researchers from Ben-Gurion University of the Negev in Israel have developed a wearable electroencephalogram (EEG) device they claim can predict epileptic seizures up to an hour before the onset. Epiness uses machine learning algorithms to analyze brain activity and detect potential seizures, and it can send a warning to a connected smartphone. Other devices on the market can detect seizures in real-time, but can't give advance warnings. However, researchers from the University of Louisiana at Lafayette last year unveiled an AI prediction model of their own. That was said to offer a similar level of prediction accuracy to Epiness, and it can also alert patients up to an hour in advance of a seizure taking hold.

Levin Kuhlmann, PhD, with Australia's University of Melbourne, said the team's evaluation revealed, on average, a 90 percent improvement in seizure prediction performance compared to previous results. Since the contest, researchers have developed the site Epilepsyecosystem.org, "Accurate seizure prediction will transform epilepsy management by offering early warnings to patients or triggering interventions," Kuhlmann said in the release. "Our results highlight the benefit of crowdsourcing an army of algorithms that can be trained for each patient and the best algorithm chosen for prospective, real-time seizure prediction. It's about bringing together the world's best data scientists and pooling the greatest algorithms to advance epilepsy research. The hope is to make seizures less like earthquakes, which can strike without warning, and more like hurricanes, where you have enough advance warning to seek safety."

Sponsored by MathWorks, the National Institutes of Health (NINDS), the American Epilepsy Society, and the University of Melbourne, the competition attracted 478 teams and 646 competitors from around the world. The algorithms I developed in MATLAB scored highest among individual participants and third highest in the competition overall. The EEG data came from a long-term study conducted by the University of Melbourne. In this study, intracranial EEG recordings were collected from 15 epileptic patients via 16 surgically implanted electrodes sampled at 400 Hz for several months. In the original study, researchers were unable to reliably predict seizures for about 50% of the test subjects.