# Analysis and prediction of schizophrenia patients based on high-order graph attention generative adversarial networks

## TL;DR

This paper proposes a new deep learning model to predict schizophrenia using brain network features from EEG data, achieving high accuracy in diagnosis.

## Contribution

A novel high-order graph attention generative adversarial network is introduced for schizophrenia prediction using EEG data.

## Key findings

- The model achieves 93.5% AUC and 93.0% MAP in the Theta frequency band for schizophrenia prediction.
- The model's image quality coefficients correlate with PANSS scores in Gamma and Theta bands.
- The model outperforms existing methods in accuracy and realism of generated persistence images.

## Abstract

Generative Adversarial Networks, a popular deep learning method, have achieved excellent performance in both classification and prediction tasks. However, there have been relatively few applications of generative adversarial networks to EEG data. To study the effect of high-order brain functional networks on schizophrenia patients, a high-order graph attention generative adversarial network prediction model is proposed, and the generator of the model utilizes graph attention networks and long short-term memory networks to capture the high-order topological features of persistence images for early diagnosis and prediction of schizophrenia patients. The research results on the five frequency bands of schizophrenia show that the proposed prediction model performs best in the Theta frequency band, with AUC and MAP values reaching 93.5% and 93.0%, respectively, and an average accuracy of 91.5%, both of which are superior to the selected comparison methods. Moreover, the image quality coefficient is used to quantify the realism and clarity of the images generated by the model. the image quality coefficients of schizophrenia patients were significantly correlated with the PANSS total scores in the Gamma and Theta bands, which provided a new idea for generative adversarial networks in the prediction of schizophrenia high-order topological features.

The online version contains supplementary material available at 10.1038/s41598-025-15602-8.