Towards using the spatio-temporal properties of eye movements to classify visual field defects

Perimetry---assessment of visual field defects (VFD)---requires patients to be able to maintain a prolonged stable fixation, as well as to provide feedback through motor response. These aspects limit the testable population and often lead to inaccurate results. We hypothesized that different VFD would alter the eye-movements in systematic ways, thus making it possible to infer the presence of VFD by quantifying the spatio-temporal properties of eye movements. We developed a tracking test to record participant's eye-movements while we simulated different gaze-contingent VFD. We tested 50 visually healthy participants and simulated three common scotomas: peripheral loss, central loss and hemifield loss. We quantified spatio-temporal features using cross-correlogram analysis, then applied cross-validation to train a decision tree algorithm to classify the conditions. Our test is faster and more comfortable than standard perimetry and can achieve a classifying accuracy of ∼90% (True Positive Rate = ∼98%) with data acquired in less than 2 minutes.