从EEG信号和PPG信号中基于深度学习的发作检测使用LSTM和CNN模型
1Professor, Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Coimbatore, Tamil Nadu - 641048, India.
The International journal of neuroscience
|February 11, 2026
概括
这项研究引入了一种混合深度学习模型,使用脑电图 (EEG) 和光电图 (PPG) 信号进行准确的实时发作检测,改进了传统方法.
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,会导致重复发作,影响生活质量.
- 目前的诊断依赖于脑电图 (EEG) 监测,这对实时检测有局限性.
- 微妙的EEG信号可以使发作检测具有挑战性.
研究的目的:
- 开发一种混合深度学习框架,用于自动检测发作.
- 集成脑电图 (EEG) 和光电图 (PPG) 信号,以提高检测准确度.
- 通过利用多式联络生物信号,改进现有的发作检测方法.
主要方法:
- 开发了一种混合深度学习模型,将卷积神经网络 (CNN) 结合起来用于空间模式提取和长短期记忆 (LSTM) 网络用于时间依赖性分析.
- 使用多模电脑电图和PPG信号,并将PPG纳入补充生理数据.
- 应用了标准的数据预处理技术,包括过,规范化,细分和增强.
主要成果:
- 与最先进的方法相比,混合CNN-LSTM模型在检测发作方面表现出卓越的性能.
- 评估指标包括准确性,精度,回忆,F1分数,科恩的卡帕,MCC和CSI证实了该模型的稳定性.
- 包括PPG信号显著加强了该模型的辨别能力,特别是在微妙的扣押模式.
结论:
- 拟议的多式联机深度学习系统为实时发作检测提供了可靠和高效的解决方案.
- 该框架具有强大的潜力,可以集成到可穿戴设备和临床医疗保健平台中,以持续监测患者.
- 利用多式联络生物信号可以提高自动发作检测系统的准确性和稳定性.
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