Early diagnosis of psychiatric conditions can be enhanced by taking into account eye movement behavior. However, the implementation of prediction algorithms which are able to assist physicians in the diagnostic is a difficult task. In this paper we propose, for the first time, an automatic approach for classification of multiple psychiatric conditions based on saccades. In particular, the goal is to classify 6 medical conditions: Alcoholism, Alzheimer's disease, opioid dependence (two groups of subjects with measurements respectively taken prior to and after administering synthetic opioid), Parkinson's disease, and Schizophrenia. Our approach integrates different feature spaces corresponding to complementary characterizations of the saccadic behavior. We define a multi-view model of saccades in which the feature representations capture characteristic temporal and amplitude patterns of saccades. Four of the current most advanced classification methods are used to discriminate among the psychiatric conditions and leave-one-out cross-validation is used to evaluate the classifiers. Classification accuracies well above the chance levels are obtained for the different classification tasks investigated. The confusion matrices reveal that it is possible to separate conditions into different groups. We conclude that using relatively simple descriptors of the saccadic behavior it is possible to simultaneously classify among 6 different types of psychiatric conditions. Conceptually, our multi-view classification method excels other approaches that focus on statistical differences in the saccadic behavior of cases and controls because it can be used for predicting unseen cases. Classification integrating different characterizations of the saccades can actually help to predict the conditions of new patients, opening the possibility to integrate automatic analysis of saccades as a practical procedure for differential diagnosis in Psychiatry. 
Multi-view classification of psychiatric conditions based on saccades