融合多模态生理信号，用几何随机模型预测癫痫猝死与中风风险。
Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability
- 基于黎曼流形与分数阶随机过程建模多源生理信号。
- 在MULTI-CLARID数据集上准确率提升，识别出可解释的生物标志物。
- 适合神经-自主神经系统疾病早期预警与风险分层研究者使用。
癫痫猝死（SUDEP）和急性缺血性中风是涉及皮层、脑干及自主神经系统复杂交互的致命性疾病。本文提出一种统一的几何-随机多模态深度学习框架，整合EEG、ECG、呼吸、SpO2、EMG和fMRI信号，以建模SUDEP与中风易感性。方法结合黎曼流形嵌入、李群不变特征表示、分数阶随机动力学、哈密顿能量流建模及跨模态注意力机制。中风传播通过结构脑图上的分数阶流行病扩散模型刻画。在MULTI-CLARID数据集上的实验表明，该框架提升了预测准确性，并提取出由流形曲率、分数阶记忆指数、注意力熵和扩散中心性等构成的可解释生物标志物。所提方法为神经-自主系统障碍的早期检测、风险分层与可解释建模提供了数学严谨基础。
原文摘要 · Abstract (English)
Sudden Unexpected Death in Epilepsy (SUDEP) and acute ischemic stroke are life-threatening conditions involving complex interactions across cortical, brainstem, and autonomic systems. We present a unified geometric-stochastic multimodal deep learning framework that integrates EEG, ECG, respiration, SpO2, EMG, and fMRI signals to model SUDEP and stroke vulnerability. The approach combines Riemannian manifold embeddings, Lie-group invariant feature representations, fractional stochastic dynamics, Hamiltonian energy-flow modeling, and cross-modal attention mechanisms. Stroke propagation is modeled using fractional epidemic diffusion over structural brain graphs. Experiments on the MULTI-CLARID dataset demonstrate improved predictive accuracy and interpretable biomarkers derived from manifold curvature, fractional memory indices, attention entropy, and diffusion centrality. The proposed framework provides a mathematically principled foundation for early detection, risk stratification, and interpretable multimodal modeling in neural-autonomic disorders.
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