基于联合学习的预测和管理系统与基于混合和搜索和相互信息 (HAS-MI) 的特征选择方法相结合
Mohd Abdul Rahim Khan1, Khaled Mahmoud Heba2,3, Abbass Hassan Abbass4,3
1Department of Electrical Engineering and Computer Science, College of Engineering, A'sharqiyah University, Ibra, 400, Sultanate of Oman. mohd.khan@asu.edu.om.
Scientific reports
|December 30, 2025
概括
这项研究引入了EpilepNet-LD与联合学习 (FL) 以使用电脑电图 (EEG) 数据进行准确的实时发作检测. 这种新的方法增强了患者的隐私,并在识别发作模式时实现了高精度.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 是一种神经系统疾病,导致发作,影响全球的日常生活.
- 目前的发作检测方法缺乏准确性,适用性和高效的特征选择.
- 传统的深度学习模型与EEG的时间和空间特征作斗争.
研究的目的:
- 开发一种新的,保护隐私的框架,用于用于发作发作检测的分散式EEG分析.
- 为了提高实时发作检测的准确性和效率.
主要方法:
- 拟议的EpilepNet-LD框架与联邦学习 (FL) 集成,用于分散的EEG分析.
- 利用混合的和搜索和相互信息 (HSA-MI) 技术,以实现最佳的EEG特征选择 (时间,光谱,空间).
- 采用混合深度学习架构,将长短期内存 (LSTM) 用于时间和DenseNet-121用于空间特征提取.
主要成果:
- 在发作检测中实现了99.41%的准确性和99.50%的灵敏性.
- 与现有最先进的方法相比,表现出优越的性能.
- FL和HSA-MI的方法提高了稳定性和精度.
结论:
- 该EpilepNet-LD框架提供了一个可靠的,高精度的解决方案,用于实时检测发作.
- 集成的FL和HSA-MI方法确保了患者隐私和协作模式培训.
- 这一进步支持改善的监测和患者管理.
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