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Researchers have created a "computational model" which can more accurately predict when an epileptic patient will experience their next seizure. This is according to a study published in IEEE Transactions on Knowledge and Data Engineering.
According to the Centers for Disease Control and Prevention (CDC), around 2.3 million US adults and 467,711 children suffer from epilepsy - one of the most common neurological disorders caused by temporary disturbances to the nerve cells in the brain.
These nerve cell disruptions can cause a person to experience recurrent seizures that can last from a few seconds to a few minutes.
Shouyi Wang, assistant professor of Industrial and Manufacturing Systems Engineering at the university and lead author of the study, says that the new model can predict epileptic seizures by using an individual's electroencephalography (EEG) readings.
Researchers are continuously looking for new ways to predict epileptic seizures, and some have even looked into the use of "seizure dogs" as indicators for the convulsions. But Wang says predicting seizures can be a testing process.
"The challenge with seizure prediction has been that every epileptic is different. Some patients suffer several seizures a day. Others will go several years without experiencing a seizure," he says.
"But if we use the EEG readings to build a personalized data profile, we're better able to understand what's happening to that person."
The computational model is made up of a series of EEG wires that are embedded within a cap that is placed upon the patient's head. This cap then sends brain readings to a computer, and the patient's risk of seizure can be determined.
For their research, the investigators tested their model on 10 epileptic individuals. From this, they found that the model could predict whether a patient will experience a seizure up to 30 minutes before it occurs with 70% or more accuracy.
Wang says that through collecting more and more personalized medical data from epileptic patients, the prediction accuracy performance of the model can be improved.
"As a society, we've gotten really good at looking at the big picture. We can tell you the likelihood of suffering a heart attack if you're over a certain age, of a certain weight and if you smoke. But we have only started to personalize that data for individuals who are all different."
Victoria Chen, professor and chairwoman of the Industrial and Manufacturing Systems Engineering Department notes that the researcher's model gives hope to those who suffer from epilepsy, and that the computational model "might be used to predict other life-threatening episodes of diseases."
Medical News Today recently reported on a study suggesting that young men could reduce their risk of developing epilepsy later in life with vigorous exercise.
Written by Honor Whiteman
Copyright: Medical News Today
Not to be reproduced without the permission of Medical News Today.
Online Seizure Prediction Using an Adaptive Learning Approach, Wang, Shouyi; Chaovalitwongse, Wanpracha Art; Wong, Stephen, published in IEEE Transactions on Knowledge and Data Engineering, 28 October 2013. Abstract
Study looks at better prediction for epileptic procedures through adaptive learning approach, news release from the University of Texas Arlington, accessed 22 November 2013.
Visit our Epilepsy category page for the latest news on this subject.
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