Classification of schizophrenia based on RAnet-ET: Resnet based attention network for eye-tracking.

Abstract

Objective: There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical eye movements compared with healthy individuals. To address this need, we have developed a classification model based on eye-tracking data to assist physicians in the intelligent auxiliary diagnosis of schizophrenia.

Approach: This study employed three eye-tracking experiments-Picture-Free Viewing, Smooth Pursuit Tracking, and Fixation Stability-to collect eye-tracking data from patients with schizophrenia and healthy controls. The eye-tracking data of 292 participants (133 healthy controls and 159 patients with schizophrenia) were recorded. Utilizing eye-tracking data in picture-free viewing, we introduce a Resnet-based Attention Network for Eye-Tracking (RAnet-ET) integrated with the attention mechanism. RAnet-ET was trained by employing multiple loss functions to classify patients with schizophrenia and healthy controls. Furthermore, we proposed a classifier for handling multimodal features that combines specific features extracted from the well-trained RAnet-ET, 100 eye-tracking variables extracted from three eye-tracking experiments, and 19 MATRICS Consensus Cognitive Battery scores.

Main results: The RAnet-ET achieved good performance in classifying schizophrenia, yielding an accuracy of 89.04%, a specificity of 90.56%, and an F1 score of 87.87%. The classification results based on multimodal features demonstrated improved performance, achieving 96.37% accuracy, 96.87% sensitivity, 95.87% specificity, and 96.37% F1 score.

Significance: By integrating attention mechanisms, we designed RAnet-ET, which achieved good performance in classifying schizophrenia from free-viewing eye-tracking data. The synergistic combination of specific features extracted from the well-trained RAnet-ET, MCCB scores, and eye-tracking variables achieved exceptional classification performance, distinguishing individuals with schizophrenia from healthy controls. This study underscores the potential of our approach as a pivotal asset for the diagnosis of schizophrenia.