---
title: "ECG-based AI for Obstructive Sleep Apnea Detection: Mechanism-Oriented Diagnostic Meta-Analysis"
id: "pubmed-42747635"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42747635"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42747635/"
doi: "10.1007/s11325-026-03798-6"
published_at: "2026-09-16T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# ECG-based AI for Obstructive Sleep Apnea Detection: Mechanism-Oriented Diagnostic Meta-Analysis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42747635
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42747635/)
- **DOI:** [10.1007/s11325-026-03798-6](https://doi.org/10.1007%2Fs11325-026-03798-6)
- **Published At:** 2026-09-16T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- This meta-analysis evaluated **ECG**-based artificial intelligence (AI) models for segment-level detection of **obstructive sleep apnea (OSA)** and sought a mechanism-oriented interpretation of how models detect events. - The review followed PRISMA 2020 and PRISMA-DTA guidance and searched PubMed, Scopus, Web of Science, and IEEE Xplore for relevant studies using physiological signals and AI for segment-level OSA detection. - Thirteen studies met inclusion criteria and were pooled using a standardized approach that reconstructed 2 × 2 contingency tables to enable cross-study comparability. - Pooled diagnostic performance was high: pooled sensitivity 0.88 and pooled specificity 0.89. The summary receiver operating characteristic (SROC) curve showed excellent discrimination with AUC > 0.90. - Between-study heterogeneity was described as moderate. Despite different model architectures, performance consistency suggested reliance on stable physiological signal patterns rather than dataset-specific artifacts. - The authors interpret that AI systems likely do not detect airway collapse directly but instead identify downstream physiological responses, notably autonomic nervous system changes reflected in cardiovascular signals such as heart rate variability. - The analysis emphasizes that segment-level AI detection using physiological signals achieves high diagnostic accuracy, while highlighting the need to understand the physiological basis of AI signals for clinical translation. - Ethical declarations: no institutional review required because only publicly available data were used; authors declared no competing interests. - Key indexed terms include **Artificial intelligence**, **Electrocardiography**, **Heart rate variability**, **Autonomic nervous system**, and **Sensitivity and Specificity**.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Physiology, Faculty of Dentistry, Yozgat Bozok University, Yozgat, Türkiye. draysebulut@gmail.com. * 2 Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Yozgat Bozok University, Yozgat, Türkiye. * PMID: **42747635** * DOI: [ 10.1007/s11325-026-03798-6 ](https://doi.org/10.1007/s11325-026-03798-6) Item in Clipboard Meta-Analysis # ECG-based artificial intelligence for obstructive sleep apnea detection: a mechanism-oriented diagnostic meta-analysis Ayşe Bulut et al. Sleep Breath. 2026. Show details Display options Display options Format Abstract PubMed PMID Sleep Breath Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Sleep+Breath%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Sleep+Breath%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) . 2026 Sep 16;30(5):260. doi: 10.1007/s11325-026-03798-6. ### Authors [Ayşe Bulut](https://pubmed.ncbi.nlm.nih.gov/?term=Bulut+A&cauthor_id=42747635)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42747635/#short-view-affiliation-1 "Physiology, Faculty of Dentistry, Yozgat Bozok University, Yozgat, Türkiye. draysebulut@gmail.com."), [Ömer Engin Bulut](https://pubmed.ncbi.nlm.nih.gov/?term=Bulut+%C3%96E&cauthor_id=42747635)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42747635/#short-view-affiliation-2 "Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Yozgat Bozok University, Yozgat, Türkiye.") ### Affiliations * 1 Physiology, Faculty of Dentistry, Yozgat Bozok University, Yozgat, Türkiye. draysebulut@gmail.com. * 2 Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Yozgat Bozok University, Yozgat, Türkiye. * PMID: **42747635** * DOI: [ 10.1007/s11325-026-03798-6 ](https://doi.org/10.1007/s11325-026-03798-6) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Objective:** Artificial intelligence (AI)-based approaches have shown promising performance in the detection of obstructive sleep apnea (OSA) using electrocardiography (ECG)-based physiological signals. However, existing studies primarily focus on diagnostic accuracy, while the underlying physiological mechanisms driving AI-based detection remain largely unexplored. This study aimed to evaluate the diagnostic performance of segment-level AI models and to provide a mechanism-oriented interpretation of their physiological basis. **Methods:** A systematic review and diagnostic meta-analysis were conducted in accordance with PRISMA 2020 and PRISMA-DTA guidelines. PubMed, Scopus, Web of Science, and IEEE Xplore databases were systematically searched. Studies utilizing AI models for segment-level OSA detection based on physiological signals were included. Sensitivity and specificity values were extracted, and 2 × 2 contingency tables were reconstructed using a standardized approach to enable cross-study comparability. A bivariate random-effects model was applied, and summary receiver operating characteristic (SROC) curves were generated. **Results:** Thirteen studies met the inclusion criteria. The pooled sensitivity and specificity were 0.88 and 0.89, respectively, indicating high diagnostic performance. The SROC curve demonstrated excellent discriminative ability (AUC > 0.90), with moderate between-study heterogeneity. Notably, consistent performance across diverse model architectures suggests that AI models rely on stable physiological signal patterns rather than dataset-specific features. **Conclusions:** AI-based models using physiological signals achieve high diagnostic accuracy for segment-level OSA detection. Importantly, these findings suggest that AI systems may not directly detect airway obstruction but instead may identify downstream physiological responses, particularly autonomic nervous system alterations reflected in cardiovascular signals. **Keywords:** Artificial intelligence; Autonomic nervous system; Diagnostic performance; Electrocardiography; Heart rate variability; Meta-analysis; Obstructive sleep apnea; Physiological signals. © 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declarations. Institutional review board statement: Because this study analyzed publicly available data and did not involve human participants or patient-level information, institutional ethical approval was not required. This study was conducted in accordance with the Helsinki Declaration. Competing interest: The authors declare no competing interests. ## References 1. 1. Dempsey JA, Veasey SC, Morgan BJ et al (2020) Pathophysiology of sleep apnea. Physiol Rev 90(1):47–112 - [DOI](https://doi.org/10.1152/physrev.00043.2008) 2. 1. Somers VK, White DP, Amin R et al (2008) Sleep apnea and cardiovascular disease: An American heart association/American college of cardiology foundation scientific statement from the American heart association council for high blood pressure research professional education committee, council on clinical cardiology, stroke council, and council on cardiovascular nursing in collaboration with the national heart, lung, and blood institute national center on sleep disorders research (national institutes of health). Circulation 118(10):1080–1111 - [DOI](https://doi.org/10.1161/circulationaha.107.189420) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/18725495/) 3. 1. Jordan AS, McSharry DG, Malhotra A (2014) Adult obstructive sleep apnoea. Lancet 383(9918):736–747 - [DOI](https://doi.org/10.1016/s0140-6736\(13\)60734-5) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/23910433/) 4. 1. Senaratna CV, Perret JL, Lodge CJ et al (2017) Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med Rev 34:70–81 - [DOI](https://doi.org/10.1016/j.smrv.2016.07.002) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/27568340/) 5. 1. Kapur VK, Auckley DH, Chowdhuri S et al (2017) Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med 13(3):479–504 - [DOI](https://doi.org/10.5664/jcsm.6506) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/28162150/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/5337595/) Show all 35 references ## Publication types * Meta-Analysis Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Meta-Analysis%22%5Bpt%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Meta-Analysis) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) * Systematic Review Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Systematic+Review%22%5Bpt%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Systematic+Review) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) ## MeSH terms * Artificial Intelligence* Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Artificial+Intelligence%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Artificial+Intelligence) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) * Electrocardiography* / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Electrocardiography%2Fmethods%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Electrocardiography) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) * Humans Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Humans%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Humans) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) * Sensitivity and Specificity Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Sensitivity+and+Specificity%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Sensitivity+and+Specificity) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) * Sleep Apnea, Obstructive* / diagnosis Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Sleep+Apnea%2C+Obstructive%2Fdiagnosis%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Sleep+Apnea%2C+Obstructive) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) * Sleep Apnea, Obstructive* / physiopathology Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Sleep+Apnea%2C+Obstructive%2Fphysiopathology%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Sleep+Apnea%2C+Obstructive) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42747635/) [x] Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM **Send To** * [Clipboard](https://pubmed.ncbi.nlm.nih.gov/42747635/) * [Email](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42747635%2F%23open-email-panel) * [Save](https://pubmed.ncbi.nlm.nih.gov/42747635/) * [My Bibliography](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42747635%2F%23open-bibliography-panel) * [Collections](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42747635%2F%23open-collections-panel) * [Citation Manager](https://pubmed.ncbi.nlm.nih.gov/42747635/) [x] NCBI Literature Resources [MeSH](https://www.ncbi.nlm.nih.gov/mesh/) [PMC](https://www.ncbi.nlm.nih.gov/pmc/) [Bookshelf](https://www.ncbi.nlm.nih.gov/books) [Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). 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