通过建模脑电图中隐含的神经事件与通道关系，提升阿尔茨海默病分类准确率。
LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
- 基于贝叶斯动态系统，从多通道脑电图直接推断隐含神经事件及它们的关联结构。
- 在两个真实阿尔茨海默病数据集上，分类性能优于主流基线方法。
- 结果可解释性强，能揭示群体间动态差异的时序与网络特征，适合临床研究者使用。
阿尔茨海默病（AD）会改变大脑电生理活动并破坏多通道脑电图（EEG）的动力学特性，因此基于脑电图的精准、临床可用诊断在筛查和疾病监测中日益重要。然而，许多现有方法依赖黑箱分类器，未显式建模决策背后的潜在事件时机与跨通道协同机制。为解决这一问题，我们提出LERD——一种端到端的贝叶斯潜在事件-关系动力系统，无需事件或交互标注即可直接从多通道脑电图中推断潜在神经事件及其关系结构。LERD结合连续时间事件推断模块与随机事件生成过程，捕捉灵活的时间模式，同时引入电生理启发的动力学先验以规范学习。我们进一步提供理论分析，推导出基于初值问题（IVP）的KL正则化项与推断关系动态的稳定性保证。在合成基准与两个真实世界阿尔茨海默病脑电图队列上的大量实验表明，LERD持续优于强基线，并生成与生理一致的事件速率、时机与图结构总结，有助于刻画群体水平的动力学差异。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for screening and disease monitoring. However, many existing approaches rely on black-box classifiers and do not explicitly model the latent event timing and cross-channel coordination behind their decisions. To address these limitations, we propose LERD, an end-to-end Bayesian latent event--relational dynamical system that infers latent neural events and their relational structure directly from multichannel EEG without event or interaction annotations. LERD combines a continuous-time event inference module with a stochastic event-generation process to capture flexible temporal patterns, while incorporating an electrophysiology-inspired dynamical prior to guide learning in a principled way. We further provide theoretical analysis that yields a tractable IVP-based KL regularizer and stability guarantees for the inferred relational dynamics. Extensive experiments on synthetic benchmarks and two real-world AD EEG cohorts demonstrate that LERD consistently outperforms strong baselines and yields physiology-aligned rate, timing, and graph summaries that help characterize group-level dynamical differences.
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