ArticleComputational intelligence and neuroscience2021
Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Accuracy of deep learning in diagnosis of apnea syndrome: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG.Frontiers in artificial intelligence · 2026Article
- SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.IEEE journal of biomedical and health informatics · 2025Article
- EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.Biomedical engineering online · 2024Article
- Using spectral continuity to extract breathing rate from heart rate and its applications in sleep physiology.Frontiers in physiology · 2024Article
- Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques.Frontiers in artificial intelligence · 2024Article
- ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis.Computers in biology and medicine · 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
- 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
- Sex differences in rTMS treatment response: A deep learning-based EEG investigation.Brain and behavior · 2022Article
- A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.BioMed research international · 2022Review
- Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring.Frontiers in public health · 2022Article
- ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen's kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.
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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.