An automatic detection method for schizophrenia based on abnormal eye movements in reading tasks

Abstract

Schizophrenia is a common mental disease worldwide. To assist doctors in diagnosing schizophrenia through objective biological indicators, an automatic detection method for schizophrenia is proposed in this work. The proposed automatic detection method, which is based on the abnormal eye movements of schizophrenic patients in reading tasks, includes three parts: valid reading state trajectory extraction, eye movement trajectory extraction, and model construction based on abnormal eye movements in schizophrenic patients. The valid reading state trajectory extraction algorithm is proposed to divide the reading process into a valid reading state and an invalid reading state based on the visual perception of the subjects in the reading process. For the eye movement trajectory extraction, an eye center positioning algorithm based on the double ring of the limbic boundary is proposed for the valid reading state. Based on the extracted eye movement trajectory, models from three aspects of abnormalities of schizophrenic patients during reading are built: the merged eye-head movement, relative eye movement, and comprehensive reading performance. The features extracted from the three models are combined with a pattern recognition classifier to realize the automatic diagnosis of schizophrenia (the support vector machine, random forest, and adaptive boosting are used in this work). The video dataset used in this experiment was recorded using 40 subjects (20 schizophrenic patients and 20 healthy controls) from the Psychiatry Department of the Mental Health Center. The experimental results show that the detection accuracy of the automatic detection method for schizophrenia can reach 96.25%, which indicates that the proposed method can be used as a computer-aided diagnosis method for schizophrenia.