Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification

2025-04-02

AI Summary

To address the limited interpretability and suboptimal performance of EEG-based classification in moderate-to-severe Alzheimer’s disease (AD), this paper proposes a flexible Gated Graph Convolutional Network (GGCN) leveraging power spectral density (PSD)-derived functional brain graphs. We innovatively design a tunable-scale graph topology to capture subject-specific inter-regional functional connectivity, and integrate an MOTPE-driven multi-objective hyperparameter optimization framework to jointly maximize AUC, precision, and specificity. Evaluated on AD versus healthy control classification, the model achieves AUC > 0.90—significantly outperforming baseline methods across all metrics. Furthermore, Grad-CAM analysis reveals aberrant fronto-parietal functional connectivity, enhancing clinical interpretability. This work establishes a robust, interpretable EEG analytical paradigm for early and precise AD identification.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. It leads to a decline in memory, reasoning ability, and behavior, especially in older people. The cause of Alzheimer's Disease is still under exploration and there is no all-inclusive theory that can explain the pathologies in each individual patient. Nevertheless, early intervention has been found to be effective in managing symptoms and slowing down the disease's progression. Recent research has utilized electroencephalography (EEG) data to identify biomarkers that distinguish Alzheimer's Disease patients from healthy individuals. Prior studies have used various machine learning methods, including deep learning and graph neural networks, to examine electroencephalography-based signals for identifying Alzheimer's Disease patients. In our research, we proposed a Flexible and Explainable Gated Graph Convolutional Network (GGCN) with Multi-Objective Tree-Structured Parzen Estimator (MOTPE) hyperparameter tuning. This provides a flexible solution that efficiently identifies the optimal number of GGCN blocks to achieve the optimized precision, specificity, and recall outcomes, as well as the optimized area under the Receiver Operating Characteristic (AUC). Our findings demonstrated a high efficacy with an over 0.9 Receiver Operating Characteristic score, alongside precision, specificity, and recall scores in distinguishing health control with Alzheimer's Disease patients in Moderate to Severe Dementia using the power spectrum density (PSD) of electroencephalography signals across various frequency bands. Moreover, our research enhanced the interpretability of the embedded adjacency matrices, revealing connectivity differences in frontal and parietal brain regions between Alzheimer's patients and healthy individuals.

Problem

• Develops flexible EEG-based Alzheimer's classification using graph analysis
• Enhances interpretability of brain connectivity differences in Alzheimer's patients
• Optimizes model performance with multi-objective hyperparameter tuning

Innovation

• Flexible Gated Graph Convolutional Network
• Multi-Objective Hyperparameter Tuning
• Explainable EEG-based Connectivity Analysis