Detection Model of Depression Based on Eye Movement Trajectory

Abstract

Eye movement trajectories of depressed patients and normal persons are different. The eye-tracking data obtained by the eye tracker can adequately summarize the characteristics of the eye movement trajectory. Based on the characteristics of eye movement trajectory, this paper proposes a new depression detection model by using an artificial neural network, which can better assist doctors in the diagnosis of depression. First, we extract the feature of eye movement trajectory, which obtains from time-series data recording the trajectory of the eye. Then, we convert the data from three-dimensional to two-dimensional, and perform feature extraction and transformation. Finally, we propose a new depression detection model by using artificial neural networks. The experimental results show that the best result of the model evaluation is 83.17%, which can effectively assist doctors in the diagnosis of depression.