DyssomniaNN is a multi‑scale computational model that integrates Borbely’s two‑process sleep regulation, Saper’s hypothalamic flip‑flop circuit, and a corticothalamic spiking neural network to simulate human sleep disorders. The model reproduces empirical EEG spectra across five clinical cohorts—healthy control, delayed‑phase circadian rhythm disorder, chronic insomnia, paradoxical insomnia, and narcolepsy type 1—within a 72‑hour photoperiod. Introducing the Well‑Rested Index, the simulations show that total sleep time does not reliably predict restorative recovery, as exemplified by paradoxical insomnia’s normal sleep duration but low restoration and narcolepsy’s severe nocturnal fragmentation.
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. 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.
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: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.
arXiv:2607. 23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics.
ECG‑SDI is a single‑lead electrocardiography framework that provides continuous sleep‑depth profiling and digital biomarker extraction. Trained on 12,392 overnight polysomnographic recordings, the two‑stage deep learning model achieved strong correlations with conventional sleep stages (0.828–0.848) and high REM classification AUROCs (0.954–0.977). The approach identified a disturbed sleep subtype linked to poorer subjective quality and higher mortality, and demonstrated measurable improvements in sleep‑depth metrics after CPAP therapy, suggesting its utility for home‑based sleep monitoring.
arXiv:2606. 07351v1 Announce Type: cross Abstract: Classification of sleep stages is one of the most important diagnostic approaches for a variety of sleep-related disorders.
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:2607. 10439v2 Announce Type: replace-cross Abstract: We model human motor cortex, recorded during rest and motor-imagery BCI conditions, as a port-Hamiltonian system: a conservative interconnection (skew-symmetric coupling between band-limited neural phasors) together with a dissipative port whose state-dependent decay is set by a graph-neural-network surrogate.
arXiv:2606. 10889v1 Announce Type: cross Abstract: Early detection of neurodegeneration remains a critical clinical challenge.
arXiv:2606. 00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication across brain regions.
The study compares six state‑of‑the‑art sleep‑staging foundation models on the MESA polysomnography dataset under three signal conditions: EEG only, ECG only, and EEG+ECG. Results show that EEG alone yields the highest accuracy (BIOT macro‑F1 = 0.7237), while switching to ECG alone incurs a consistent accuracy loss of about 0.35 macro‑F1 and reduces data rate to one‑third. Adding ECG to EEG offers little benefit for most models, indicating that EEG carries most of the sleep‑staging signal and that wearable‑compatible ECG alone is a viable but less accurate alternative.