Early detection of pediatric epilepsy possible through 'deep learning' technique

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. Most BECT patients self-heal in puberty, but the disease can cause verbal dysfunction, attention deficit and language impairment in 18 to 25 percent of patients. Studies have shown that drug treatment could improve language skill and normalize centrotemporal spikes in electroencephalograph (EEG) tests, so it's important to distinguish epilepsy patients from healthy people. While studies have found that magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) are promising for differentiating BECT patients from healthy people, these imaging techniques are mainly based on a doctor's knowledge and diagnostic ability, and hence also have limitations such as low accuracy. Few studies have focused on developing machine learning methods that can recognize BECT patients.