Researchers have outlined a new framework that may pave the way for a smartphone app that could predict seizures in advance.
Every morning, you wake up and check the weather app on your smartphone to see if it will rain. If the forecast probability is high enough, let’s say 80 percent, you decide to bring an umbrella to work.
Previous attempts to develop prediction systems for epilepsy patients have almost always failed…
Now, imagine being able to do the same thing to check and see if you’ll have a seizure. An app providing a daily seizure forecast would be life changing for people with epilepsy.
The researchers, whose framework appears in a paper in Brain Journal of Neurology, foresee that users will be able to enter information about their seizure activity, medication, and other lifestyle factors that can be combined with environmental data and brain recordings. The app will then aggregate the information to tell the user how likely they are to have a seizure that day.
Depending on personal preference and the acuity of the forecasting model, seizure likelihood can be presented as five risk levels, corresponding to 20 percent increments of increasing seizure likelihood. After long-term monitoring the forecasts can be personalized, in response to individual seizure patterns.
Providing patients with probabilities, rather than certainties, is a more realistic way to forecast seizures. People with epilepsy can then tailor their lifestyles to minimize their risk. For instance, only exercising when their seizure forecast drops below 20 percent, or taking additional protective measures once the forecast climbs above 80 percent.
Previous attempts to develop prediction systems for epilepsy patients have almost always failed due to low volumes of data. To combat this problem, long-term analysis was critical—so researchers used the world’s longest continuous database of brain recordings as their dataset.
The data were recorded from the surface over the brain during a previous trial for an implantable seizure warning device, which ran three years and involved fifteen patients with drug resistant epilepsy.
The results show that seizure prediction is feasible; however, the performance was not successful for everyone. The researchers have now used the same data to show seizure forecasting is viable for more people. The framework provides better predictive performance than any other method previously trialed.
Algorithm predicts epileptic seizures in real-time
To date, much of the work in the field of seizure prediction has focused on answering a definitive question—will someone have a seizure, or not? But that ignores the subtleties of brain dynamics, researchers say. For instance, a seizure is more likely, but not certain when the brain enters an excited state. Forcing a forecast to take on only two possible outcomes means that these highly excitable states are misdiagnosed, making it difficult to refine or improve predictions.
To reflect the brain’s changing state a more useful question to ask is: “What is the probability a person will have a seizure in the next hour?” Treating seizure likelihood as a continuum, rather than a duality (you will/will not have a seizure), makes forecasts more flexible. Many factors affect the excitability of the brain and, as a result, someone’s risk of seizure changes throughout the day.
The work builds on a previous study in Brain Journal of Neurology in which researchers proved that some people have seizures far more often at certain times of the day. They expected seizures to cluster at certain times of day, but were surprised by just how distinctive patterns were between patients. The next question was: “How can these patterns be used to improve prediction strategies?”
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The latest results show patterns of seizure occurrence can be combined with existing models to provide patients with more useful, flexible forecasts. The hope is that these forecasts will become seamlessly integrated into the lives of people with epilepsy.
Source: Philippa Karoly for University of Melbourne
