# Sleep Staging

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Scientific Reports Oct 20, 2026
Rat sleep staging holds up when the drug changes: trained on saline alone, F1 falls less than 1% under psilocybin and MDL100907
On more than 1.1 million annotated rat EEG/EMG segments, a robustness-oriented deep learning pipeline reaches 94.3% F1 for four-class sleep staging, 7.3 and 6.4 points above SlumberNet's 87.0% and random forest's 87.9%. Most automated stagers were evaluated on random splits that preserve the training distribution, and degrade when subjects or drug conditions shift. The pipeline combines a global context vector, supervised contrastive learning and a dual-branch classifier to learn representations insensitive to subject and treatment; trained on saline recordings only and tested on unseen animals, every metric drops by less than 1%.
On more than 1.1 million annotated rat EEG/EMG segments, a robustness-oriented deep learning pipeline reaches 94.3% F1 for four-class sleep staging, 7.3 and 6.4 points above SlumberNet's 87.0% and random forest's 87.9%. Most automated stagers were evaluated on random splits that preserve the training distribution, and degrade when subjects or drug conditions shift. The pipeline combines a global context vector, supervised contrastive learning and a dual-branch classifier to learn representations insensitive to subject and treatment; trained on saline recordings only and tested on unseen animals, every metric drops by less than 1%.
Charles University
Vojtěch Brejtr（First author）Lenka Lhotská（Corresponding author）
Sleep Staging Supervised Contrastive Learning Cross-Attention Domain Adaptation
npj Digital Medicine Oct 30, 2026
Sleep staging stops picking electrodes: any EEG-EOG mix scores a night, and resolution drops to seconds
Sleep staging has long depended on fixed electrode sets and 30-second expert-scored epochs, which makes multi-center studies hard to harmonize. AnySleep fuses any number of EEG and EOG channels through channel attention, trained and validated on 20,589 overnight recordings from 28 datasets, matching or slightly exceeding U-Sleep at 30-second epochs and staying strong with no EOG or a single derivation. At sub-30-second resolution, the share of expert-annotated arousal time overlapping predicted Wake rises from 7.7% at 30 seconds to 57.7% near 2 seconds.
Sleep staging has long depended on fixed electrode sets and 30-second expert-scored epochs, which makes multi-center studies hard to harmonize. AnySleep fuses any number of EEG and EOG channels through channel attention, trained and validated on 20,589 overnight recordings from 28 datasets, matching or slightly exceeding U-Sleep at 30-second epochs and staying strong with no EOG or a single derivation. At sub-30-second resolution, the share of expert-annotated arousal time overlapping predicted Wake rises from 7.7% at 30 seconds to 57.7% near 2 seconds.
Utrecht University FH Aachen
Niklas Grieger（First author, Corresponding author）Stephan Bialonski（Corresponding author）
AnySleep Sleep Staging Attention Mechanism U-Net Sleep Disorders
SLEEP Sep 15, 2026
Foundation Models in Sleep Research: Opportunities and Limitations
This Commentary reviews recently published sleep foundation models by comparing their training cohorts, assessment frameworks, and reported performance, and applies an existing sleep foundation model without fine-tuning to an independent cohort of patients with Narcolepsy Type 1 (n = 51) and healthy controls (n = 28), finding modest zero-shot sleep-staging performance below supervised methods on the same cohort and minimal improvement in disorder classification from PSG-derived embeddings beyond demographic baselines, concluding that sleep foundation models are not yet suitable for clinical deployment.
This Commentary reviews recently published sleep foundation models by comparing their training cohorts, assessment frameworks, and reported performance, and applies an existing sleep foundation model without fine-tuning to an independent cohort of patients with Narcolepsy Type 1 (n = 51) and healthy controls (n = 28), finding modest zero-shot sleep-staging performance below supervised methods on the same cohort and minimal improvement in disorder classification from PSG-derived embeddings beyond demographic baselines, concluding that sleep foundation models are not yet suitable for clinical deployment.
Ente Ospedaliero Cantonale University Hospital of Bern University of Bern University of Brescia Università della Svizzera italiana University of Applied Sciences and Arts of Southern Switzerland
Annina Helmy（First author）
Foundation Models Sleep Staging Clinical Evaluation of Sleep Foundation Models

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Rat sleep staging holds up when the drug changes: trained on saline alone, F1 falls less than 1% under psilocybin and MDL100907

Sleep staging stops picking electrodes: any EEG-EOG mix scores a night, and resolution drops to seconds

Foundation Models in Sleep Research: Opportunities and Limitations