Early detection of the most common form of epilepsy in children is possible through “deep learning,” a new machine learning tool that teaches computers to learn by example, according to a new study that includes researchers from Georgia State University.
This study proposed a novel classification method that combines the predictions of three different types of brain imaging data: handcrafted features (51 brain characteristics associated with the disease) from MRI, MRI and fMRI. The researchers passed the imaging data respectively through three different machine learning methods, including two specially designed deep neural networks, and then combined the outcomes into a final decision-making neural network to determine whether a patient has BECT. They suggest that merging data from multiple views could provide complementary information and improve the diagnosis of this common form of epilepsy in children.
The research team used data from West China Hospital in Chengdu, China, which included 40 BECT patients and 40 healthy people. They applied the existing deep learning algorithm but modified it to fit the needs of this study. The findings are published in the journal IET Computer Vision.
“Deep learning could assist with early detection of benign epilepsy with centrotemporal spikes, which improves the patient’s overall outcomes and reduces suffering,” said Dr. Yi Pan, corresponding author of the study, Regents’ Professor of Computer Science and chair of the Department of Computer Science at Georgia State. “Early detection means early treatment. Early treatment means better health for these children.”
SOURCE: Georgia State University
