SynthesisSensors (Basel, Switzerland)2019
A Systematic Review of Detecting Sleep Apnea Using Deep Learning.
Synthesis in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 180 citations in OpenAlex.
- ECG-based artificial intelligence for obstructive sleep apnea detection: a mechanism-oriented diagnostic meta-analysis.Sleep & breathing = Schlaf & Atmung · 2026Pooled it
- Enhancing airway obstruction diagnosis with multimodal 3D shape analysis.International journal of computer assisted radiology and surgery · 2026Article
- World of ScoreCraft: Novel Multi-Scorer Experiment on the Impact of a Decision Support System in Sleep Staging.Journal of sleep research · 2026Article
- Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome.Frontiers in medicine · 2026Article
- Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG.Frontiers in artificial intelligence · 2026Article
- Automated OSAHS detection from ECG using temporal convolutional network.Scientific reports · 2025Article
- Artificial intelligence based platform for the automatic and simultaneous explainable detection of apnoea, oxygen desaturation, and artefacts in paediatric polygraphy exams (REST).Scientific reports · 2025Article
- Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review.World journal of radiology · 2025Article
- Article
- SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.IEEE journal of biomedical and health informatics · 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
- 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
- ApneaWhisper: Transformer-Based Audio Segmentation for Fine-Grained Non-Invasive Sleep Apnea Detection.Nature and science of sleep · 2025Article
- AI-driven sleep apnea screening with overnight blood oxygen saturation: current practices and future directions.Frontiers in digital health · 2025Review
- SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.Sensors (Basel, Switzerland) · 2024Article
- An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals.Scientific reports · 2024Article
- ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text.Transactions on machine learning research · 2024Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
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.
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.