An Analysis of Eye-Tracking Features and Modelling Methods for Free-Viewed Standard Stimulus: Application for Schizophrenia Detection

Currently psychiatry is a medical field lacking an automated diagnostic process. The presence of a mental disorder is established by observing its typical symptoms. Eye-movement specifics have already been established as an “endophenotype” for schizophrenia, but an automated diagnostic process of eye-movement analysis is still lacking. This article presents several novel approaches for the automatic detection of a schizophrenic disorder based on a free-view image test using a Rorschach inkblot and an eye tracker. Several features that enabled us to analyse the eye-tracker signal as a whole as well as its specific parts were tested. The variety of features spans global (heat maps, gaze plots), sequences of features (means, variances, and spectra), static (x and y signals as 2D images), dynamic (velocities), and model-based (limiting probabilities and transition matrices) categories. For each set of features, a proper modelling and classification method was designed (convolutional, recurrent, fully connected and combined neural networks; Hidden Markov models). By doing so, it was possible to find the importance of each feature and its physical representation using k-fold cross validation and a paired t-test. The dataset was sampled on 22 people with schizophrenia and 22 healthy individuals. The most successful approach was based on heat maps using all data and convolutional networks, reaching a 78.8% accuracy, which is a 10.5% improvement over the reference method. From all tested methods, there are two in an 85% accuracy range and over fifteen others in a 75% accuracy range at a 10% significance level.