Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep 30;24(9):e39452.
doi: 10.2196/39452.

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review

Daniela Ferreira-Santos  1   2 , Pedro Amorim  1   2   3 , Tiago Silva Martins  2 , Matilde Monteiro-Soares  1   2   4 , Pedro Pereira Rodrigues  1   2
Affiliations

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review

Daniela Ferreira-Santos et al. J Med Internet Res. .

Abstract

Background: American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.

Objective: We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.

Methods: We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.

Results: Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.

Conclusions: Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.

Trial registration: PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339.

Keywords: machine learning; obstructive sleep apnea; polysomnography; systematic review.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Flow diagram of the study selection process.
See this image and copyright information in PMC

References

    1. Kapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K, Harrod CG. Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American academy of sleep medicine clinical practice guideline. J Clin Sleep Med. 2017 Mar 15;13(3):479–504. doi: 10.5664/jcsm.6506. doi: 10.5664/jcsm.6506.jc-17-00035 - DOI - DOI - PMC - PubMed
    1. Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MS, Morrell MJ, Nunez CM, Patel SR, Penzel T, Pépin J, Peppard PE, Sinha S, Tufik S, Valentine K, Malhotra A. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respiratory Med. 2019 Aug;7(8):687–98. doi: 10.1016/s2213-2600(19)30198-5. - DOI - PMC - PubMed
    1. Kendzerska T, Mollayeva T, Gershon AS, Leung RS, Hawker G, Tomlinson G. Untreated obstructive sleep apnea and the risk for serious long-term adverse outcomes: a systematic review. Sleep Med Rev. 2014 Feb;18(1):49–59. doi: 10.1016/j.smrv.2013.01.003.S1087-0792(13)00007-5 - DOI - PubMed
    1. Lin H, Lai C, Lin P, Friedman M, Salapatas AM, Chang H, Lin M, Chin C. Clinical prediction model for obstructive sleep apnea among adult patients with habitual snoring. Otolaryngol Head Neck Surg. 2019 Jul 02;161(1):178–85. doi: 10.1177/0194599819839999. - DOI - PubMed
    1. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016 Dec 05;5(1):210. doi: 10.1186/s13643-016-0384-4. https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s136... 10.1186/s13643-016-0384-4 - DOI - DOI - PMC - PubMed

Publication types

LinkOut - more resources