ArticleSensors (Basel, Switzerland)2021
Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Redefining obstructive sleep apnea diagnosis: An attention augmented CNN-BiLSTM hybrid alternative to traditional PSG testing.MethodsX · 2026Article
- Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG.Frontiers in artificial intelligence · 2026Article
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- Unsupervised deep learning of electrocardiograms enables scalable human disease profiling.NPJ digital medicine · 2025Article
- Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier.Entropy (Basel, Switzerland) · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.BioMed research international · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models-two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) models, which were previously proposed in the OSA detection domain. We have chosen four ensemble techniques-majority voting, sum rule and Choquet integral based fuzzy fusion and trainable ensemble using Multi-Layer Perceptron (MLP) for our case study. All the experiments are conducted on the benchmark PhysioNet Apnea-ECG Database. Finally, we have achieved highest OSA detection accuracy of 85.58% using the MLP based ensemble approach. Our best result is also able to surpass many of state-of-the-art methods.
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Registered trials
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