I’ve written a few times recently about new technologies that are hoping to help people suffering from epilepsy. For instance, a recent project from UCL utilized machine learning to better spot epilepsy in children.
The research, which was a collaborative project between Young Epilepsy, UCL Great Ormond Street Institute of Child Health and the University of Cambridge, focused on Focal Cortical Dysplasia, which is a major cause of epilepsy in children. It describes the way the brain fails to form normally, and because the abnormalities tend to be small, they tend to be very difficult to pick up on MRI scans.
What would make such work even cooler is if they could predict the onset of a fit before it occurs, raising the potential of then intervening to prevent the seizure. That’s exactly what a team from Rice University set out to do. The team developed an algorithm to predict when seizures might occur. After several iterations and a large dose of testing, it was able to predict seizures at least two minutes before their onset.
Bringing it to market
A team now hope to bring such predictive capabilities to market via a smartphone app that can predict seizures in advance. The project, which was documented in a recent paper, aims to combine information about seizure activity, medication, and other lifestyle factors with things such as environmental data and brain recordings. The app will use this data to predict the likelihood of a seizure occurring that day.
The app aims to provide users with probability ratings across five distinct risk levels (of 20% increments), but the app can become more honed as it tunes itself to your individual behaviors and seizure patterns. The developers hope it will allow users to modify their lifestyle according to the risk of an event occurring.
The app was trained using the world’s longest continuous database of brain recordings. The data was generated through a previous, three year long, trial that looked at developing an implantable seizure warning device. That trial showed the potential to predict seizures, but the results were not accurate all the time.
The team believe that by providing a probable chance of seizures occurring rather than attempting absolute answers they can provide a more useful service for users.
The project builds upon previous work by the team that shed light on the intricacies of epilepsy. It showed that some people are much more likely to have seizures at certain times of the day, and this insight fed into the creation of the app.
By providing flexible, yet accurate, forecasts, the team hope that they will significantly help people to live the kind of lives they hope to and manage their epilepsy more effectively.