联合深度学习模型用于使用电脑电图 (EEG) 信号检测发作
G R Abijith1, S Jothi2, Chandrasekar A3
1Department of Information Technology, St. Joseph's Institute of Technology, Chennai, India.
Neurological research
|September 23, 2025
概括
一个新的联合学习启用统一变压器模型增强了发作检测. 这种人工智能方法提高了准确性和数据隐私,以更好地管理神经系统疾病.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 是一种慢性神经系统疾病,影响生活质量.
- 传统的发作检测方法在数据隐私和捕获复杂的EEG信号关系方面扎.
研究的目的:
- 提出一个支持联合学习的统一变压器模型,以改进发作检测.
- 解决数据隐私,安全和特征提取在脑电图 (EEG) 分析中的局限性.
主要方法:
- 使用Paillier同型加密的联合学习来保护隐私的协作培训.
- 采用自适应式噪声过,独立组件分析和多尺度波段系数用于信号预处理和特征提取.
- 集成了基于混合图的注意力框架与边缘增强图卷积网络和光谱图注意力,用于空间和频率特征分析.
- 应用了统一变压器模型来准确地对发作进行分类,捕获时间和空间的EEG依赖性.
主要成果:
- 该模型实现了高性能指标:98.91%的准确性,98.93%的安全性和98.82%的精度在三个数据集上.
- 与现有的发作检测模型相比,其表现优越.
结论:
- 联合学习启用统一变压器模型在发作发作检测方面取得了重大进展.
- 联合学习和深度学习的整合提高了医疗保健应用的适用性.
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