A researcher at Rice University’s Brown School of Engineering and an alumna of her lab have the first validation of their program to assess the risk of seizures in patients with epilepsy.
In a preliminary study, their Epilepsy Seizure Assessment Tool (EpiSAT) proved equally able or better than 24 specialized epilepsy clinicians at using patients’ histories to identify periods of heightened propensity for seizures.
The researchers’ automated machine-learning algorithm correctly identified changes in seizure risk — improvement, worsening or no change — in more than 87% of cases. They achieved those results by analyzing 120 seizures from four “synthetic” diaries and 120 seizures from real seizure diaries gathered by SeizureTracker.com, one of the largest electronic seizure diaries in the world. EpiSAT showed “substantial observed agreement” with clinicians more than 75% of the time, they reported.
The results appear in the journal Epilepsia.
“One challenge in treating people with epilepsy is that, like the chance of rain, there has never been a good way to quantify seizure risk and to determine whether apparent changes in seizure frequency reflect chance or actual improvement or worsening in their clinical state,” said Vikram Rao, chief of the epilepsy division and an associate professor of neurology at UCSF. “The algorithm that Dr. Chiang developed in this study directly addresses that clinical dilemma.”