Xu Qingjie, An Libing, Yang Haiqiang, Hong Keum-Shik
School of Automation, Institute for Future, Qingdao University, Qingdao, China.
Shandong Key Laboratory of Industrial Control Technology, Qingdao, China.
Front Neurosci. 2025 Jul 2;19:1555657. doi: 10.3389/fnins.2025.1555657. eCollection 2025.
Alzheimer's disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare professionals due to the subtle nature of early brain changes. Currently, electroencephalography (EEG) is widely used in the study of neurodegenerative diseases. However, most existing research relies solely on functional connectivity methods to infer inter-regional brain connectivity, overlooking the importance of spatial connections. Moreover, many existing approaches fail to fully integrate multi-frequency EEG features, limiting the comprehensive understanding of dynamic brain activity across different frequency bands. This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.
This paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. This method integrates both functional and structural connectivity to more thoroughly capture the relationships among brain regions. By extracting differential entropy (DE) features from five distinct frequency bands of EEG signals for each segment and using graph convolutional networks (GCNs) to aggregate these features, the model effectively distinguishes between AD and healthy controls (HC).
The outcomes show that the developed model outperforms existing methods, achieving 96.15% accuracy and 98.74% AUC in AD and HC classification.
These findings highlight the potential of the MF-MGCN model as a clinical tool for Alzheimer's disease diagnosis. This approach could help clinicians detect Alzheimer's at earlier stages, enabling timely intervention and personalized treatment plans.
阿尔茨海默病（AD）主要通过认知功能衰退来识别。由于早期脑变化的微妙性质，在早期阶段诊断AD对研究人员和医疗保健专业人员都构成了重大挑战。目前，脑电图（EEG）广泛应用于神经退行性疾病的研究。然而，大多数现有研究仅依赖功能连接方法来推断脑区之间的连接，而忽略了空间连接的重要性。此外，许多现有方法未能充分整合多频EEG特征，限制了对不同频段动态脑活动的全面理解。本研究旨在通过开发一种新颖的基于图的深度学习模型来解决这些局限性，该模型充分利用多频EEG数据中的功能和结构信息。
本文介绍了一种用于AD诊断的基于多频EEG数据的多图卷积网络（MF-MGCN）模型。该方法整合了功能连接和结构连接，以更全面地捕捉脑区之间的关系。通过从每个EEG信号段的五个不同频段提取微分熵（DE）特征，并使用图卷积网络（GCN）聚合这些特征，该模型有效地区分了AD患者和健康对照（HC）。
结果表明，所开发的模型优于现有方法，在AD和HC分类中准确率达到96.15%，AUC达到98.74%。
这些发现突出了MF-MGCN模型作为阿尔茨海默病诊断临床工具的潜力。这种方法可以帮助临床医生在更早阶段检测出阿尔茨海默病，从而能够及时进行干预并制定个性化治疗方案。
