Artificial Intelligence for Sleep Instability and Motor Phenotyping: Clinical Translation Beyond Sleep Staging

Clicks: 3
ID: 315243
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #160 of 189 articles by views in Sleep & breathing = Schlaf & Atmung

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 189 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Sleep medicine has rapidly adopted artificial intelligence, but most applications still prioritize automated sleep staging or single summary indices, limiting clinical translation when symptoms arise from within-stage dynamics. This review proposes a physiology-grounded framework in which artificial intelligence targets sleep microstructure and nocturnal motor activity as temporally structured expressions of sleep–wake control. We discuss how transient arousals and cyclic alternating pattern activity can be modeled as time-resolved instability trajectories rather than reduced to hourly counts, and why grounding models in established constructs improves interpretability and trust. We then examine motor events across the continuum from leg movements to periodic limb movements and large muscle group movements, emphasizing that periodicity, clustering, state dependence, and coupling to cortical and autonomic activation convey more clinical information than event counts alone. Because autonomic surges are measurable outside the laboratory, we highlight multimodal approaches integrating electroencephalography, electromyography, actigraphy, cardiopulmonary signals, and wearable photoplethysmography to infer instability and movement–autonomic coupling in ambulatory settings. Finally, we translate these outputs into clinician-readable phenotypes that may refine diagnosis, prognosis, and treatment stratification, and we define priorities for the field: harmonized labeling standards, multi-center external validation, calibration across age and comorbidity, explainable artificial intelligence approaches, and deployment as decision-support tools that complement expert judgment.
Reference Key
openalex_W7162758580 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Maria P. Mogavero, Oliviero Bruni, Giuseppe Lanza, Ugo Faraguna, Alessandro Silvani, Raffaele Ferri
Journal Sleep & breathing = Schlaf & Atmung
Year 2026
DOI
10.1093/sleep/zsag149
URL
https://doi.org/10.1093/sleep/zsag149
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.