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ECG-SDI: Single-lead Electrocardiogram Enables Continuous Sleep Depth Index Profiling and Scalable Digital Biomarkers for Sleep Health — arXiv:2610.23727

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.23727
License (as archived): CC0-1.0
Archived: 2026-10-19T09:21:31.940Z
SHA-256 of the archived text: 9f8af99a6dc4616000bdd169dd7e252a3594c7f52d7823d737c26d17009c15ff
Stable URL of this page: https://metraipolis.com/documento/dc_49effa703239

How to cite

ECG-SDI: Single-lead Electrocardiogram Enables Continuous Sleep Depth Index Profiling and Scalable Digital Biomarkers for Sleep Health — arXiv:2610.23727 — arXiv (en), https://arxiv.org/abs/2610.23727 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_49effa703239 (Metraipolis archive dc_49effa703239, sha256 9f8af99a6dc4616000bdd169dd7e252a3594c7f52d7823d737c26d17009c15ff)

BibTeX

@misc{dc49effa703239,
  title = {ECG-SDI: Single-lead Electrocardiogram Enables Continuous Sleep Depth Index Profiling and Scalable Digital Biomarkers for Sleep Health — arXiv:2610.23727},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.23727},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_49effa703239; sha256 9f8af99a6dc4616000bdd169dd7e252a3594c7f52d7823d737c26d17009c15ff},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.23727. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation. We developed ECG-SDI, a single-lead electrocardiography (ECG) framework for continuous sleep-depth profiling and digital biomarker extraction. Using 12,392 overnight polysomnographic recordings, we trained and validated a two-stage deep learning model supervised by conventional sleep-stage labels. Correlations with ordered stages excluding rapid eye movement (REM) sleep were 0.828 in the pooled internal test set and 0.848 in the external Cleveland Family Study; corresponding REM classification AUROCs were 0.954 and 0.977. Overnight trajectories captured within-stage variation. Biomarker-based phenotyping identified a disturbed subtype associated with poorer subjective sleep quality and higher all-cause mortality in the Sleep Heart Health Study (adjusted hazard ratio, 1.20; 95% CI, 1.05-1.38). Among 507 participants with paired recordings before and after continuous positive airway pressure (CPAP), normalized sleep-depth area increased by 6.1% and shallow-sleep burden below 0.3 decreased by 16.0%. Exploratory analyses linked reductions in approximate entropy and depth variability to reduced oxygen desaturation and increased sleep efficiency, respectively (Spearman rho = 0.24 and 0.12); both associations remained positive after covariate adjustment. Mean ECG-SDI within N2 increased by 0.017 (95% CI, 0.004-0.030). A wearable proof-of-concept illustrated the acquisition-to-report workflow. These findings support ECG-based sleep phenotyping and further evaluation of CPAP response monitoring at home.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Signal Processing (eess.SP) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.23727 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:17:41.730Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).