KUMC Researchers Announce Breakthrough Method For Predicting Epileptic Seizures

KANSAS CITY, Kan. -- Through the application of mathematics, digital signal processing and computer technology, researchers at the University of Kansas Medical Center believe they have unlocked the complicated secret of predicting when a patient with epilepsy is about to have a seizure.

In recent studies of 125 seizures monitored and recorded at KU Medical Center, the technique has predicted seizures by up to three minutes in advance, according to Ivan Osorio, MD, director of the Comprehensive Epilepsy Center and associate professor of neurology. "This is the first time anyone has been able to predict when a seizure is about to happen. Prior to this, seizures were considered unpredictable," Osorio said.

The research is sponsored by funding from the KU Research Development Fund; College of Arts and Sciences; the surgery and neurology foundations at KU Medical Center; the National Institutes of Health; and the National Science Foundation's Research Experience for Undergraduate fund. Osorio and Mark Frei, PhD, from the Medical Center campus, along with John Ralston, professor of physics and astronomy; Josef Dorfmeister, professor of mathematics; and David Lerner, associate professor of mathematics, all of the KU-Lawrence campus, are co-developers of the technique to predict seizures. The project has been in development at KU Medical Center over the past three and a half years. Standard electroencephalograms (EEGs) can record when a seizure occurs. EEGs are tools used to measure electrical activity within the brain. By using the algorithm, researchers can now predict when a seizure is imminent.

"The algorithm measures brain activity and separates the signals into seizure and non-seizure components. This creates a ratio of seizure to non-seizure elements," Osorio said. While seizure and non-seizure components are present in the brain of a patient with epilepsy at all times, the moment the seizure components reach a certain critica

Contact: Dann Hayes
University of Kansas

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