ReviewHealthcare (Basel, Switzerland)2021
A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems.
Review in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Status and Opportunities of Machine Learning Applications in Obstructive Sleep Apnea: A Narrative Review.medRxiv : the preprint server for health sciences · 2025Article
- A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data.Singapore medical journal · 2025Article
- Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review.Computational and structural biotechnology journal · 2025Review
- Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach.IEEE open journal of engineering in medicine and biology · 2025Article
- A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.medRxiv : the preprint server for health sciences · 2024Article
- Towards automatic home-based sleep apnea estimation using deep learning.NPJ digital medicine · 2024Article
- Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.Sleep medicine · 2024Article
- Long-term health outcomes for patients with obstructive sleep apnea: placing the Agency for Healthcare Research and Quality report in context-a multisociety commentary.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.Frontiers in pediatrics · 2024Article
- Bringing At-home Pediatric Sleep Apnea Testing Closer to Reality: A Multi-modal Transformer Approach.Proceedings of machine learning research · 2023Article
- Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea.Diagnostics (Basel, Switzerland) · 2023Article
- Sparse Decomposition of Heart Rate Using a Bernoulli-Gaussian Model: Application to Sleep Apnoea Detection.Sensors (Basel, Switzerland) · 2023Article
- Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile.International journal of environmental research and public health · 2023Article
- Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor.Frontiers in neuroscience · 2023Article
- Sleep disorder and apnea events detection framework with high performance using two-tier learning model design.PeerJ. Computer science · 2023Article
- Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare.Pervasive and mobile computing · 2022Article
- Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial intelligence for predicting adherence to CPAP therapy in obstructive sleep apnoea: A systematic review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.