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一种基于CBAM-3D CNN-LSTM模型的发作预测方法

Xiang Lu¹, Anhao Wen¹, Lei Sun²

¹College of Electronic and Information EngineeringShandong University of Science and Technology Qingdao Shandong 266590 China.

IEEE journal of translational engineering in health and medicine

|July 10, 2023

概括

这项研究引入了一种新的深度学习模型,用于使用电脑电图 (EEG) 信号预测发作. 该模型准确地识别了发作预测,改善了患者的安全和健康结果.

科学领域:

神经学神经学
人工智能的人工智能
生物医学信号处理

背景情况:

是一种常见的神经系统疾病,其特点是突然,反复发作.
及时预测发作对于减少患者受伤和改善健康结果至关重要.
现有的深度学习模型往往忽略了脑电图 (EEG) 信号中的空间特征.

研究的目的:

开发和评估一个深度学习模型,用于准确预测发作.
利用EEG信号的时间和空间特征来提高预测准确度.
加强从EEG数据中提取关键的间接和前间接特征.

主要方法:

使用短时间里埃变换 (STFT) 预处理EEG信号.
使用3D卷积神经网络 (CNN) 来进行特征提取.
采用双向长短期记忆 (Bi-LSTM) 网络进行分类.
整合一个卷积块注意模块 (CBAM) 以专注于相关的空间和通道信息.

主要成果:

在预测方面取得了97.95%的准确性.
在发作检测方面表现出98.40%的灵敏度.
报告了0.017小时-1的低错误报警率.
在公开的CHB-MIT头皮EEG数据集上验证了11名患者.

结论:

拟议的CBAM-3D CNN-LSTM模型通过整合时间和空间EEG特征,有效预测发作.
注意力机制增强了模型提取关键区分特征的能力.
这种方法具有显著的潜力,可以提高患者的安全性和的管理.

关键词:

3DCNNN 在线播放这就是为什么CBAM是CBAM.这是一个EEGEEGEEGEEGEEGEEGEEG.这是LSTM的LSTM.抢劫预测预测的预测

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