Parametric and non-parametric stochastic anomaly detection in analysis of eye-tracking data

Two methods for distinguishing between healthy subjects and patients diagnosed with Parkinson´s disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener-type model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters due to Parkinsonism. For accurate estimation of the model parameters, visual stimuli designed to excite the essential nonlinear dynamics of the oculomotor system are used and a method of generating the stimuli is presented. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects attempting to track the visual stimuli. Both methods show promising results, where healthy individuals and patients diagnosed with Parkinson´s disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson´s disease.