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STCGRU:基于CNN和BiGRU的混合模型,用于轻度认知障碍诊断

Hao Zhou¹, Liyong Yin², Rui Su³

¹Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China.

Computer methods and programs in biomedicine

|March 12, 2024

概括

一种新的混合深度学习模型,即时空间卷积封闭反复单元网络 (STCGRU),使用EEG数据准确诊断轻度认知障碍 (MCI). 这种先进的方法显示出早期检测和预防阿尔茨海默病进展的巨大潜力.

科学领域:

神经科学是一个神经科学.
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:

轻度认知障碍 (MCI) 的早期诊断对于预防阿尔茨海默病 (AD) 的进展至关重要.
脑电图 (EEG) 数据为认知障碍检测提供了宝贵的见解.

研究的目的:

提出一种新的混合深度学习模型,即时空间卷积封闭循环单元网络 (STCGRU),用于早期诊断MCI.
用公开可用的,预处理的EEG数据集来评估STCGRU模型的稳定性和准确性.

主要方法:

STCGRU模型整合了三个卷积神经网络 (CNN) 模块和一个双向封闭循环单元 (BiGRU) 模块.
该模型旨在从原始EEG数据中提取空间和时间特征.
进行了废弃实验,以评估单个STCGRU模块的贡献.

主要成果:

在十倍的交叉验证后,STCGRU模型实现了99.95%的平均分类准确率和0.9989的kappa值.
该模型在用于MCI诊断的同一EEG数据集上,与最先进的方法相比,显示出更高的性能.
在STCGRU有效地直接从原始EEG数据中提取时空特征.

结论:

拟议的STCGRU混合深度学习模型可以从原始EEG数据中准确直接分类MCI.
STCGRU模型对于早期诊断轻度认知障碍患者具有很高的实用价值.
这种方法对神经退行性疾病研究中的早期干预策略具有重大前景.

关键词:

双向封闭循环单元 (BIGRU) 是一个双向封闭循环单元.卷积神经网络 (CNN) 是一种神经网络.电脑电图 (EEG) 是一种电脑电图.混合型深度学习模型.轻度认知障碍 (MCI) 是一种轻度认知障碍.

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