用于发作检测的扩展卷积的动态图卷积网络
Xiaoxiao Zhang1, Chenyun Dai2, Yao Guo2
1Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Bioengineering (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种新的动态图形卷积网络 (DGDCN),用于通过脑电图 (EEG) 信号改善自动发作的检测. 通过动态学习信号连接和捕获远程依赖,DGDCN模型提高了准确性.
科学领域:
- 神经科学与生物医学工程
- 信号处理和机器学习
背景情况:
- 脑电图 (EEG) 对于测量大脑活动至关重要,并且已被广泛用于自动检测发作.
- 使用卷积神经网络 (CNN),长期内存 (LSTM) 和图形卷积网络 (GCN) 的现有方法由于对欧几里德结构或固定图形连接的假设而面临限制.
研究的目的:
- 解决当前自动检测算法的局限性.
- 提出一个新的动态图形卷积网络以扩展卷积 (DGDCN),以增强基于EEG的发作检测.
主要方法:
- 开发了一个动态图卷积网络与扩展卷积 (DGDCN) 算法.
- 采用时空注意力机制来动态构建特定任务的GCN相邻矩阵.
- 整合了一个扩展的卷积模块来扩大受体场并捕获远程时间依赖.
主要成果:
- 在12秒的EEG段中,DGDCN模型实现了AUC值88.7%,在60秒的EEG段中达到90.4%.
- 与目前最先进的扣押检测方法相比,在TUSZ数据集上表现出具有竞争力的表现.
结论:
- 拟议的DGDCN模型有效地捕捉了EEG信号中的局部空间和时间依赖以及远程时间信息.
- 动态图结构和扩展卷曲为自动发作检测的准确性提供了显著的进步.
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