Researchers at the University of Sydney are using artificial intelligence to develop a method to predict seizures that will not require surgical intervention.
Dr Omid Kavehei from the Faculty of Engineering and IT and the University of Sydney Nano Institute said: "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."
In a paper published last May in Neural Networks, Dr Kavehei and his team have proposed a seizure-prediction method that can alert people with epilepsy within 30 minutes of when a seizure may occur.
Dr Kavehei said there have been major advances in recent years.
"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," Dr Kavehei said.
Carol Ireland, chief executive of Epilepsy Action Australia, said: "Living with constant uncertainty significantly contributes to increased anxiety in people with epilepsy and their families, never knowing when the next seizure may occur.
"Even people with well controlled epilepsy have expressed their constant concern, not knowing if or when they will experience a seizure at work, school, travelling or out with friends. Any progress toward reliable seizure prediction will significantly impact the quality of life and freedom of choice for people living with epilepsy."
Dr Kavehei and lead author of the study, Nhan Duy Truong, used deep machine learning and data-mining techniques to develop a dynamic analytical tool that can read a patient's EEG, data from a wearable cap or other portable device to gather EEG data.
Wearable technology could be attached to a device based on already available technology that can potentially give a patient a 30-minute warning and percentage likelihood of a seizure. An alarm would go off between 30 and five minutes before a seizure onset, giving patients ample time to find a safe place or find a way to prevent or control the seizure.
Dr 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."
A benefit of Dr Kavehei's approach is that the system learns as brain patterns change, requiring minimum feature engineering. This results in faster and more regular updates of the information, giving patients maximum benefit from the seizure prediction system.
The next stage is to develop a physical prototype to test the system clinically with partners at the University of Sydney's medical campus.