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New MM-Net model enhances sleep staging and apnea assessment after stroke

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MM-Net, a novel multimodal multi-task network designed to improve the accuracy of sleep staging and sleep apnea assessment, particularly in patients who have experienced an ischemic stroke. This network addresses challenges in existing methods by fusing adapted EEG-EOG representations with cardiorespiratory features through cross-attention and shared temporal modeling. MM-Net demonstrates superior performance on the iSLEEPS dataset, achieving 76.3% staging accuracy and 0.825 respiratory-event AUC, and also shows strong results on the Sleep-EDF and ISRUC-I datasets. AI

IMPACT This research could lead to more accurate and integrated diagnostic tools for sleep disorders in stroke patients.

RANK_REASON The cluster contains a research paper detailing a new model for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MM-Net model enhances sleep staging and apnea assessment after stroke

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The cluster contains a research paper detailing a new model for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Md Wahiduzzaman Suva, Esm E Moula Chowdhury Abha, Md Imtiaj Alam Sajin, Md Iftekharul Mobin, Jia Uddin ·

    MM-Net: A Multimodal Multi-Task Network for Sleep Staging and Sleep Apnea Assessment After Ischemic Stroke

    arXiv:2610.36583v1 Announce Type: cross Abstract: Automated polysomnography after ischemic stroke is challenging because of altered sleep-related neural activity and concurrent respiratory disturbances. Existing stroke-specific approaches mainly address sleep-stage classification…

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