Epileptic seizures strike with little warning and nearly one third of people living with epilepsy are resistant to treatment that controls these attacks. More than 65 million people worldwide are living with epilepsy. “We are on track to develop an affordable, portable and non-surgical device that will give reliable prediction of seizures for people living with treatment-resistant epilepsy,” said Omid Kavehei from the University of Sydney in Australia. In a study published in the journal Neural Networks, researchers have proposed a generalised, patient-specific, seizure-prediction method that can alert epilepsy sufferers within 30 minutes of the likelihood of a seizure.
There had been remarkable advances in artificial intelligence as well as micro- and nano-electronics that have allowed the development of such systems, Kavehei said. “Just four years ago, you couldn’t process sophisticated AI through small electronic chips. Now it is completely accessible. In five years, the possibilities will be enormous,” Kavehei said. The study uses three data sets from Europe and the US. Using that data, the team has developed a predictive algorithm with a sensitivity of up to 81.4 percent and false prediction rate as low as 0.06 an hour. “While this still leaves some uncertainty, we expect that as our access to seizure data increases, our sensitivity rates will improve,” Kavehei said.
Researchers used deep machine learning and data-mining techniques to develop a dynamic analytical tool that can read a patient’s electroencephalogram (EEG) data from a wearable cap or other portable device to gather EEG data. Wearable technology could be attached to an affordable device that could give a patient a 30-minute warning and percentage likelihood of a seizure. An alarm would be triggered between 30 and five minutes before a seizure onset, giving patients time to find a safe place, reduce stress or initiate an intervention strategy to prevent or control the seizure. Kavehei said an advantage of their system is that is unlikely to require regulatory approval, and could easily work with existing implanted systems or medical treatments.
The algorithm can generate optimised features for each patient. They do this using what is known as a ‘convolutional neural network’, that is highly attuned to noticing changes in brain activity based on EEG readings. An advantage of the methodology is that the system learns as brain patterns change, requiring minimum feature engineering. This allows for faster and more frequent updates of the information, giving patients maximum benefit from the seizure prediction algorithm.