Evidence map›Paper›PMID 41125590›Full record

ArticleNature communications2025

Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals.

Zhongxu Zhuang, Biao Xue, Qiang An, Hui Chu, Yue Zhang, Rui Chen, Jing Xu, Ning Ding, Xiaochuan Cui, E Wang and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Contactless Sleep Staging With Radar: A Transfer Learning Approach.IEEE open journal of engineering in medicine and biology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Zhongxu Zhuang *School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.ORCID http://orcid.org/0000-0001-5672-1833
Biao Xue *School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Qiang An *Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Hui ChuSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Yue ZhangSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology ZiJin College, Nanjing, China.
Rui ChenDepartment of Respiratory and Critical Care Medicine, Sleep Center, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Jing XuDepartment of Respiratory and Critical Care Medicine, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
Ning DingDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Xiaochuan CuiDepartment of General Medicine and Sleep Medicine Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.
E WangDepartment of Anesthesiology, Xiangya Hospital, Central South University, Changsha, China.ORCID http://orcid.org/0000-0001-9463-9769
Meilin WangDepartment of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0000-0002-4996-958X
Junyi XinDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Xuan YangSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Yan XuSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Yaxian LiSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Chang-Hong FuSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Xiaohua ZhuSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Mugen PengThe State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China. pmg@bupt.edu.cn.ORCID http://orcid.org/0000-0002-4755-7231
Hong HongSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China. hongnju@njust.edu.cn.ORCID http://orcid.org/0000-0002-1528-8479

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sleep disorders affect billions globally, yet diagnostic access remains limited by healthcare resource constraints. Here, we develop a deep learning framework that analyzes respiratory signals for remote sleep health monitoring, trained on 15,785 nights of data across diverse populations. Our approach achieves robust performance in four-stage sleep classification (82.13% accuracy on internal validation; 79.62% on external validation) and apnea-hypopnea index estimation (intraclass correlation coefficients 0.90 and 0.94, respectively). Through transfer learning, we adapt the model to radar-derived respiratory signals, enabling contactless monitoring in home environments. The framework demonstrates consistent performance across demographic subgroups, supports real-time processing through self-supervised learning techniques, and integrates with a remote sleep health management platform for clinical deployment. This approach bridges critical gaps in sleep healthcare accessibility, supporting population-level screening and monitoring, paving the way for scalable sleep healthcare, and advancing sleep health equity.

Indexed as

Deep LearningHealth EquitySleepSleep Wake DisordersAdultAgedFemaleHumansMaleMiddle AgedPolysomnographyYoung Adult

Identifiers

PMID41125590
PMCPMC12546746

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.