GraM-Diff：用于基于脑电图的阿尔茨海默病数据生成与诊断的统一图-状态空间扩散框架
GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis
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中文总结 AI 辅助
本研究提出GraM-Diff框架，通过结合图卷积网络与双向Mamba状态空间块生成脑电图数据，用于阿尔茨海默病诊断，在四个基准测试中提升了分类性能与鲁棒性。
中文摘要 AI 辅助
脑电图（EEG）是一种有前景、非侵入且成本效益高的阿尔茨海默病（AD）检测模态，但深度学习方法受限于小型且不平衡的临床数据集。生成式数据增强可解决该问题，然而现有方法依赖低效的类别特定模型，或无法捕捉复杂的脑时空动态。为应对这一问题，我们提出GraM-Diff，一种统一的分类器引导图-状态空间（Graph-Mamba）扩散框架，用于脑电图合成。该框架在扩散U-Net中嵌入图卷积网络以建模电极间连接，并采用双向Mamba状态空间块实现线性复杂度的长程时序建模。隐空间分类器引导使单个模型能在共享表示中生成健康和病理脑电图，避免了碎片化的队列专属流程。在四个基于脑电图的AD基准测试中，合成数据增强提升了分类性能，相较于强大的生成基线取得了更优的Context-FID和相关分数，并在数据稀缺场景中增强了鲁棒性。
英文摘要
Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning methods are limited by small and imbalanced clinical datasets. Generative augmentation offers a solution, yet existing approaches rely on inefficient class-specific models or fail to capture complex spatial and temporal brain dynamics. To address this, we propose GraM-Diff, a unified classifier-guided Graph-Mamba diffusion framework for EEG synthesis. It embeds Graph Convolutional Networks within a diffusion U-Net to model inter-electrode connectivity and Bidirectional Mamba state-space blocks for linear-complexity long-range temporal modeling. Latent-space classifier guidance lets a single model generate both healthy and pathological EEG within a shared representation, avoiding fragmented per-cohort pipelines. Across four EEG-based AD benchmarks, synthetic augmentation improves classification, yields superior Context-FID and correlation scores over strong generative baselines, and enhances robustness in data-scarce settings.
发表机构
- Indian Institute of Technology Indore（印度理工学院印多尔分校）
机构由 AI 辅助整理，请以论文原文为准。
