Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability

TL;DR

This paper introduces a comprehensive geometric-stochastic deep learning framework that integrates multiple physiological signals to improve early prediction and understanding of SUDEP and stroke risks, providing interpretable biomarkers.

Contribution

It presents a novel multimodal deep learning approach combining advanced geometric and stochastic modeling techniques for neural-autonomic disorder prediction.

Findings

Enhanced predictive accuracy on MULTI-CLARID dataset

Identification of interpretable biomarkers from manifold curvature and diffusion centrality

Effective modeling of stroke propagation using fractional epidemic diffusion

Abstract

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.