arXiv:2603. 00190v2 Announce Type: replace-cross Abstract: Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and cohorts.
arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.
SleepFM-2 is a foundation model trained on 282,511 polysomnography recordings, covering over two million hours of multimodal sleep physiology. It outperforms its predecessor in disease prediction, sleep scoring, and event detection, and its representation improves performance across diverse tasks—including wearable sensing, subjective sleep reports, and transfer to other EEG modalities. When combined with age, sex, and BMI, the model meets stringent discrimination criteria for 215 EHR phenotypes, adding reproducible information beyond demographics for 155 of them.
arXiv:2607. 07720v1 Announce Type: cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration.
arXiv:2602. 07628v2 Announce Type: replace Abstract: While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to task-specific models that focus on localized micro-structure features.
arXiv:2607. 07720v2 Announce Type: replace-cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration.
arXiv:2607. 04851v1 Announce Type: cross Abstract: Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source domains or domain labels that are rarely available in practice.
arXiv:2606. 18506v1 Announce Type: new Abstract: Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as sleepiness and fatigue.
arXiv:2512. 14461v2 Announce Type: replace Abstract: Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care.
Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source domains or domain labels that are rarely available in practice. We tackle the stricter and more practical setting of single-source domain generalization: training on a single labeled source dataset, without domain labels or access to target data.
arXiv:2609.05550v1 Announce Type: cross Abstract: Near-infrared (NIR) video is a promising modality for contactless sleep monitoring, but recent video-based sleep staging methods often use it as a ro...
NanoSleep is a compact hybrid temporal convolutional network designed for single‑channel EEG sleep stage classification. It integrates a learnable Sinc‑convolutional front end, dual‑branch feature extraction for multi‑scale temporal and spectral representations, a gated dilated temporal convolutional backbone with channel recalibration, and a conditional random field for sequence‑level decoding. Evaluated on Sleep‑EDF and Sleep‑EDF‑Expanded datasets, NanoSleep consistently outperforms six baseline methods and demonstrates that each major component contributes to its performance.