基于高阶图的精神分裂症患者的分析和预测注意力产生对抗网络
Guimei Yin1, Mengzhen Yin2, Guangxing Guo3
1College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China. yinguimeicn@126.com.
Scientific reports
|February 2, 2026
概括
使用生成对抗网络 (GAN) 的新型深度学习模型通过分析EEG数据的脑功能网络来预测早期精神分裂症的前景,在Theta频段实现高精度.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 生成对抗网络 (GAN) 是强大的深度学习工具,在脑电图 (EEG) 数据分析中应用有限.
- 了解高阶大脑功能网络对于精神分裂症研究至关重要.
研究的目的:
- 为早期精神分裂症诊断开发和评估一个高阶图表注意力生成对抗网络 (GAN) 预测模型.
- 调查GANs在从EEG数据的持久图像中捕获高阶拓特征的实用性.
主要方法:
- 拟议的模型在它的生成器中使用图表注意力网络和长短期记忆网络.
- 来自EEG数据的持久图像用于表示高阶拓特征.
- 该模型在五个频段的精神分裂症患者EEG数据上进行了评估.
主要成果:
- 该预测模型在Theta频段实现了最佳性能,曲线下的面积 (AUC) 为93.5%,平均精度 (MAP) 为93.0%.
- 平均准确率达到91.5%,超过了比较方法.
- 图像质量系数与 PANSS 总分数在玛波段和泰达波段有显著的相关性.
结论:
- 开发的基于GAN的模型在使用EEG数据的高阶拓特征来预测精神分裂症方面表现出有效性.
- 这些发现表明了应用GAN在精神分裂症预测和理解其高阶拓特征的新方法.
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