Within a scientific research team between the College of Computer Science and Information Technology at University of Al-Qadisiyah in participation with researchers from the University of Arkansas at Little Rock in the United States, an innovative technique is presented for efficient feature selection that improve diagnosing early status of epileptic attack. Journal of Smart Health at Elsevier has published the new technology in a scientific research article. Journal of Smart Health is highly ranked with Q1 level in.

The team consisted of researchers, Professor Dr. Dhiah Al-Shammary and researcher Mr Mustafa Noman Kadhim from University of Al-Qadisiyah, in participation with researcher Mr Muhammad Sadiq and Professor Mariofanna Milanova from University of Arkansas at Little Rock in the United States.

The research included developing and presenting a new and innovative technology to determine the influential and effective features in diagnosing epileptic attack by providing smart diagnostic systems with the effective features and ignore the ineffective features in EEG signals and giving them early alarm to avoid accidents. The new system is part of a project for people with epilepsy to drive cars, which enables them to avoid accidents if they face an epileptic seizure by interfacing the early epilepsy diagnosis system with the car’s systems and issuing an instruction to drive automatically or make an emergency stop. The research team have developed the smart feature selection technique based on the mathematical Hellinger distance metric.