ArticleBioMed research international2019
Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- SDANN May Be Used as a Screening Factor of Obstructive Sleep Apnea in Adult Individuals.BioMed research international · 2026Article
- Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.Sleep & breathing = Schlaf & Atmung · 2025Article
- AI-Assisted Awake Endoscopic Video Analysis for Obstructive Sleep Apnea Detection.Nature and science of sleep · 2025Article
- ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis.Computers in biology and medicine · 2023Article
- Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study.Sensors (Basel, Switzerland) · 2023Review
- Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2023Article
- Article
- Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.Frontiers in neuroscience · 2023Article
- A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine.IISE transactions on healthcare systems engineering · 2023Article
- Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale · 2022Review
- [Sleep apnea automatic detection method based on convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.Sensors (Basel, Switzerland) · 2021Article
- A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems.Healthcare (Basel, Switzerland) · 2021Review
- A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.PloS one · 2021Article
- A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography.Digital healthArticle
- Enhancing Obstructive Apnea Disease Detection Using Dual-Tree Complex Wavelet Transform-Based Features and the Hybrid "K-Means, Recursive Least-Squares" Learning for the Radial Basis Function Network.Journal of medical signals and sensorsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep apnea (SA) is a ubiquitous sleep-related respiratory disease. It can occur hundreds of times at night, and its long-term occurrences can lead to some serious cardiovascular and neurological diseases. Polysomnography (PSG) is a commonly used diagnostic device for SA. But it requires suspected patients to sleep in the lab for one to two nights and records about 16 signals through expert monitoring. The complex processes hinder the widespread implementation of PSG in public health applications. Recently, some researchers have proposed using a single-lead ECG signal for SA detection. These methods are based on the hypothesis that the SA relies only on the current ECG signal segment. However, SA has time dependence; that is, the SA of the ECG segment at the previous moment has an impact on the current SA diagnosis. In this study, we develop a time window artificial neural network that can take advantage of the time dependence between ECG signal segments and does not require any prior assumptions about the distribution of training data. By verifying on a real ECG signal dataset, the performance of our method has been significantly improved compared to traditional non-time window machine learning methods as well as previous works.
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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.