SEEGformer:基于SEEG的个性化发作检测和针对耐药性的发作区域定位
Chengxiang Wang1, Jing Hong2, Shuyu Liu1
1Medical Robot Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
EBioMedicine
|December 18, 2025
概括
一个名为SEEGformer的AI系统准确地检测发作,并使用立体电脑图数据定位发作区. 这项技术通过为临床决策提供客观,数据驱动的见解来增强个性化的治疗.
科学领域:
- 人工智能在医学中的应用
- 神经科学是一个神经科学.
- 医学成像分析 医学成像分析
背景情况:
- 是一种复杂的神经疾病,需要精确识别发性区域 (EZ) 才能有效治疗,特别是在耐药病例中.
- 高维立体脑电图 (SEEG) 数据需要先进的分析方法来有效地检测和EZ定位.
- 个性化,数据驱动的方法对于管理耐药焦点至关重要.
研究的目的:
- 开发和验证一个人工智能系统,SEEGformer,用于使用多通道SEEG数据进行高通量发作检测和发病带定位.
- 利用先进的AI技术,包括变压器架构,分析复杂的SEEG信号表示和道间依赖关系.
- 整合MRI信息,在AI模型中构建大脑结构依赖.
主要方法:
- 利用了来自多个队伍的61名患者的SEEG数据,包括数万例异常出院.
- 采用并行变压器架构 (SEEGformer) 分析分析性SEEG信号的真实,虚拟和振幅组件.
- 整合了MRI数据和跨道注意力机制,以计算EZ局部化的发性风险得分,并使用个人患者模型进行培训和验证.
主要成果:
- 在三个临床队列中,SEEGformer的平均AUROC为0.937的发作检测和0.798的EZ定位.
- 定位性能超过了最先进的方法5%以上.
- 在不断发展的发性网络中确定了不同的相位同步模式 (P < 0.0001).
结论:
- SEEGformer提供了高解读性和可视化,增强了临床决策,用于发性区域划分和手术规划.
- 人工智能系统为优化治疗策略提供了一个客观的,数据驱动的参考.
- 目前正在进行的开发旨在使用改进的SEEGform创建一个专门用于的SEEG地图.
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