ArticleJournal of medical systems2018
Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy of deep learning in diagnosis of apnea syndrome: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- A Systematic Review of Detecting Sleep Apnea Using Deep Learning.Sensors (Basel, Switzerland) · 2019Pooled it
- An Explainable Fusion of ECG and SpOBioengineering (Basel, Switzerland) · 2025Article
- Multimodal Sleep Apnea Detection with Missing or Noisy Modalities.Proceedings of machine learning research · 2024Article
- Real-Time Obstructive Sleep Apnea Detection from Raw ECG and SpOAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Bringing At-home Pediatric Sleep Apnea Testing Closer to Reality: A Multi-modal Transformer Approach.Proceedings of machine learning research · 2023Article
- Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.Biomedical engineering letters · 2023Review
- Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of Respiratory Events during Sleep Based on Fusion Analysis and Entropy Features of Cardiopulmonary Signals.Entropy (Basel, Switzerland) · 2023Article
- Role of automated detection of respiratory related heart rate changes in the diagnosis of sleep disordered breathing.Frontiers in sleep · 2023Article
- MCFN: A Multichannel Fusion Network for Sleep Apnea Syndrome Detection.Journal of healthcare engineering · 2023Article
- Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor.Frontiers in neuroscience · 2023Article
- A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Sleep Apnea Detection Based on Multi-Scale Residual Network.Life (Basel, Switzerland) · 2022Article
- Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index.Sleep & breathing = Schlaf & Atmung · 2021Article
- [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
- Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.Journal of medical systems · 2021Article
- Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F
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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.