A Systematic Review of Detecting Sleep Apnea Using Deep Learning
Open accessAbstract
Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.
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DL algorithms have been used to detect early signs of sleep apnea.
“Additionally, DL algorithms have been used to detect early signs of sleep apnea, a common sleep disorder with potentially severe health consequences [197].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [197]
Sleep monitoring wearable devices have gained significant popularity in consumer health and sleep research in recent years as a non-invasive and convenient tool for tracking sleep patterns and assessing sleep quality.
“Sleep monitoring wearable devices have gained significant popularity in consumer health and sleep research in recent years as a non-invasive and convenient tool for tracking sleep patterns and assessing sleep quality [197,239] (Figure 7d).”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [197]
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- Authors
- Mostafa, S. S., Mendonça, F., G. Ravelo-García, A., & Morgado-Dias, F.
- Affiliations
- University of Lisbon (PT)
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas, Spain
- Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, 9000-082 Funchal, Portugal
- Corresponding author
- Mostafa, Sheikh Shanawaz
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-11-12
- Volume
- 19
- Issue
- 22
- Pages
- 4934
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6891618
- OpenAlex
- W2985634329
- Cited by
- 182
- References
- 91
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 35 · 2023: 40 · 2022: 20 · 2021: 26 · 2020: 10
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Acceda (Universidad de Las Palmas de Gran Canaria) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
- Funders
- Portuguese Foundation for Science and Technology (Projeto Estratégico UID/EEA/50009/2019)
- ARDITI - Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação (Project M1420-09-5369-FSE-000001-PhD Studentship)
- MITIExcell - Excelencia Internacional de IDT&I NAS TIC provided by the Regional Government of Madeira (Project Number M1420-01-01450FEDER0000002)
- Topics
- Health Sciences › Medicine › Physiology › Obstructive Sleep Apnea Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- sleep apnea, automated sleep scoring, polysomnography, deep learning, wearable sensors, feature extraction, sleep monitoring
- MeSH terms
- Deep Learning, Humans, Sleep Apnea Syndromes, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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