Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912755078922240 |
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| author | Girish, Preksha Mysore, Rachana U, Mahanthesha Kumar, Shrey Annigeri, Misbah Fatimah Jain, Tanish |
| author_facet | Girish, Preksha Mysore, Rachana U, Mahanthesha Kumar, Shrey Annigeri, Misbah Fatimah Jain, Tanish |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_08257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability Girish, Preksha Mysore, Rachana U, Mahanthesha Kumar, Shrey Annigeri, Misbah Fatimah Jain, Tanish Machine Learning Image and Video Processing 68T07, 68T05, 60H10, 53C21 I.2.6; I.2.7; I.5.4; I.2.10 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. |
| title | Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability |
| topic | Machine Learning Image and Video Processing 68T07, 68T05, 60H10, 53C21 I.2.6; I.2.7; I.5.4; I.2.10 |
| url | https://arxiv.org/abs/2512.08257 |