{
  "abstract": "Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care. Across centers, polysomnography (PSG) recordings are traditionally scored in 30-s epochs for pragmatic, not physiological, reasons and vary in electrode count, montage, and subject characteristics. These constraints challenge harmonized multi-center studies and the discovery of robust biomarkers on shorter timescales. We present AnySleep, a deep neural network that scores sleep from any electroencephalography (EEG) or electrooculography (EOG) data at adjustable temporal resolutions. We trained and validated the model on over 20,000 overnight recordings (>200,000 h of EEG and EOG) from 28 datasets across multiple clinics to promote robust generalization across sites. The model attains state-of-the-art performance and surpasses or equals established baselines at 30-s epochs. Performance improves with more channels, yet remains strong when EOG is absent, or only EOG or single EEG derivations (frontal, central, or occipital) are available. On sub-30-s timescales, the model captures short wake intrusions consistent with arousals and improves the prediction of pathophysiological conditions (obstructive sleep apnea, narcolepsy type 1, insomnia) over 30-s scoring. We make the model publicly available to facilitate large-scale studies with heterogeneous electrode setups and accelerate biomarker discovery in sleep.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Jülich, Germany. grieger@fh-aachen.de.",
    "Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands. grieger@fh-aachen.de.",
    "Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, Jülich, Germany. grieger@fh-aachen.de.",
    "Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Jülich, Germany.",
    "Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, Jülich, Germany.",
    "Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.",
    "Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Jülich, Germany. bialonski@fh-aachen.de.",
    "Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, Jülich, Germany. bialonski@fh-aachen.de."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Niklas Grieger",
      "orcid": "0009-0000-3774-5418"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Jannik Raskob",
      "orcid": "0009-0005-4103-4105"
    },
    {
      "affs": [
        5
      ],
      "name": "Siamak Mehrkanoon"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Stephan Bialonski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4jv4ps2y3hfja.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-03406-7",
  "fulltext_url": "https://europepmc.org/articles/PMC13722680",
  "id": "4jv4ps2y3hfja",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://europepmc.org/article/MED/42963354",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W4417473717",
  "pdf_url": "https://europepmc.org/articles/PMC13722680?pdf=render",
  "pmcid": "PMC13722680",
  "pmid": "42963354",
  "published": "2026-10-30",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/4jv4ps2y3hfja.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T03:21:21Z",
      "native_id": "MED:42963354",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42963354"
    },
    {
      "first_seen": "2026-11-04T12:21:27Z",
      "native_id": "PMC13722680",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-03406-7"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts.",
  "title_text": "AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T12:21:27Z",
  "url": "https://research.draconitic.com/paper/4jv4ps2y3hfja",
  "venue": "NPJ digital medicine",
  "versions": []
}