Scientists at the University of Aberdeen are working to develop a system that could see smartphones used to warn epilepsy sufferers of the likelihood of a seizure before it happens.
Applied mathematicians from the University’s Institute for Complex Systems and Mathematical Biology have been awarded £247,533 in funding by Swiss-based W Science Laboratories to develop a ‘proof of concept’ for a system that utilises complex or 'big data' to collect information on situations and circumstances that epilepsy patients find themselves in before a seizure occurs.
The data could be collected from a wide variety of sources - patients’ smartphones being just one example - and over time will build a picture of the type of activities that patients are involved in before or during a seizure.
This information can then be used by the system to identify and predict situations in which patients may find themselves at greater risk, and warn them to take preventative action.
“This project is about finding completely new ways of treating epilepsy by utilising complex data,” explains Dr Marco Thiel, who is working on the project with Professor Bjoern Schelter.
“What we want to do is monitor patients by using data that is captured in everyday devices that are present in their daily lives, such as a smartphone.
“We can learn a lot from a smartphone because it captures where you are, how you move, it can measure sound, light intensity and so on. Combine this with other data such as your email and social media activity and over time we can build a picture to help identify potential triggers in the form of situations and circumstances that may give rise to a seizure.
“From here we can then provide advice to patients, for example through an app on their phone or some other device, and warn them when they are at risk of a seizure and how they can modify their behaviour or change their situation to prevent this.”
Professor Schelter added: “We live in a world where there is a massive amount of data, and our research aims to use statistical techniques and other methods such as artificial intelligence to interpret large data sets.
“While we have this mathematical and statistical knowledge, what’s missing is the framework that can capture all of this complex data in one device and interact with the patient.
“This particular project is all about developing that framework, and once we have a proof of concept that we can build a device that has these capabilities in principle, we can go to the testing stage where we begin to collect data from real epilepsy patients.
“Providing everything falls into place we expect to begin conducting clinical trials within the next two years.”
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