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 · 14 citations
Medicine · Engineering · Neuroscience · #Obstructive Sleep Apnea Research #Non-Invasive Vital Sign Monitoring #EEG and Brain-Computer Interfaces

paper · doi:10.1016/j.compbiomed.2021.105124

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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• An Indoor Fall Monitoring System: Robust, Multistatic Radar Sensing and Explainable, Feature-Resonated Deep Neural Network. [europepmc]
• Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. [europepmc]
• Sleep disorder and apnea events detection framework with high performance using two-tier learning model design. [europepmc]
• Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection. [europepmc]
• 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]
• Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm. [europepmc]
• Accuracy of deep learning in diagnosis of apnea syndrome: a systematic review and meta-analysis. [europepmc]
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