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arXiv AI By 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

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MM‑Net is a multimodal, multi‑task neural network designed to simultaneously stage sleep and detect respiratory events in patients after ischemic stroke. It fuses sleep‑adapted EEG‑EOG embeddings with engineered neural descriptors and cardiorespiratory features using cross‑attention and shared temporal modeling, and employs cross‑fitting and ridge‑based embedding alignment to maintain representation consistency across patients. In a patient‑independent evaluation on the iSLEEPS dataset, MM‑Net achieved 76.3 % sleep‑stage accuracy (κ = 0.656) and a 0.825 AUC for respiratory‑event detection, outperforming baseline models; it also performed strongly on Sleep‑EDF and ISRUC‑I datasets and retained high respiratory AUCs when applied to unseen cohorts without retraining.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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