Decision support system for the diagnosis of neurological disorders based on gaze tracking
Dávid Kupás, Balázs Harangi, Gyorgy Czifra, Gábor Andrássy · 2017
Current diagnosis of neurological disorders is an expensive and time-consuming task. Our goal is to make this procedure easier and more accurate using a digital eye scanner. Our system can help in making diagnoses, assists in the practice and shortens the time needed to find the appropriate treatment. First and foremost we collect all important visual effects in the field of neurological examination and create a video to make possible the testing of the eye movement of the patient during the video. Their gaze data is collected by an appropriate eye tracker, then we analyze the gaze information in order to evaluate the mental state of the patient using machine learning based algorithms. According to the experimental results, our proposed technique can separate the healthy and ill patients from each other using their gaze data.