Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network

2021/12/06 by Quanan Yang, Lang Zou, Keming Wei +1 · 89 citations
Engineering · Medicine · Neuroscience · #Algorithm #Apnea #Artificial intelligence #Attention network #Cardiology #Computer science #EEG and Brain-Computer Interfaces #Electronic engineering #Embedded system #Engineering #Focus (optics) #Internal medicine #Lead (geology) #Medicine #Non-Invasive Vital Sign Monitoring #Obstructive Sleep Apnea Research #Obstructive sleep apnea #Pattern recognition (psychology) #Polysomnography #Residual #Sensitivity (control systems) #Sleep apnea #Test set #Wearable computer

paper · doi:10.1016/j.compbiomed.2021.105124

published in Computers in Biology and Medicine 140, 105124 (Elsevier BV)

openalex publication_date 2021/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Citations

• PhysioBank, PhysioToolkit, and PhysioNet
• ImageNet classification with deep convolutional neural networks
• Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder
• SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals

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• EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets. [europepmc]
• Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals. [europepmc]
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