Clustering Based on Eye Tracking Data for Depression Recognition

Abstract

The attention-based approach would be a good way of detecting depression, assisting medical diagnosis, and treating the patients at risk earlier. In this article, a new approach of recognizing depression is proposed, which avoids eye movement event identification and directly performs clustering based on eye tracking data to obtain regions of interesting (ROIs), and then conducts depression recognition modeling. Based on these, a novel spatiotemporal clustering algorithm was proposed, i.e., ROI Clustering with Deflection Elimination, which takes the noisy data into consideration to better describe attention patterns. On the data set with 45 depression patients and 44 healthy controls, the proposed algorithm achieved the best classification accuracy of 76.25%, which has the potential to provide methodological reference on the assessment of mental disorders based on eye movements.