一个时间频率交叉注意力网络模型用于发作检测
RuYi Wang1, Lvbo Tian2, Mengqiu Li2
1School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China. 202312490704@nuist.edu.cn.
Scientific reports
|March 14, 2026
概括
一个新的时间频率交叉注意网络 (TFCANet) 有效地融合了脑电图 (EEG) 的时间和频率域数据,以改善的检测,在临床数据集中实现高精度.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 的诊断在很大程度上依赖于脑电图 (EEG) 信号.
- 目前的脑电图分析往往不充分利用频域信息,限制了诊断准确度.
- 整合时间和频率域特征在研究中构成了重大挑战.
研究的目的:
- 开发一个先进的深度学习模型,以提高的检测.
- 为了有效地整合来自EEG信号的时间和频率域特征.
- 在全面的特征提取和融合中克服传统模型的局限性.
主要方法:
- 提出了一个新的时间频率交叉注意力网络 (TFCANet),利用剩余注意力机制.
- 使用快速里埃转换 (FFT) 将时间域EEG特征转换为频域.
- 集成的SE剩余模块用于频域和剩余窗口多头自动注意 (ResWMSA) 用于时间域特征提取.
- 利用交叉注意力来实现有效的模式间特征融合.
主要成果:
- 在HMS-有害大脑活动分类数据集 (5类任务) 上获得了96.15%的准确性.
- 在波恩大学数据集 (5类任务) 上获得了93.63%的准确性.
- 与单一模式方法相比,时间频率特征融合的表现优越.
结论:
- 该TFCANet模型成功地整合了时间和频率域EEG特征,以进行强大的发病检测.
- 时间频率特征融合显著提高了症分类的诊断准确性.
- 拟议的模型显示了改善的临床诊断的希望.
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