A systematic review of detecting sleep apnea using deep learning
Sleep apnea is a sleep related disorder that significantly affects the population.
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an …
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an …
Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram
U Erdenebayar, YJ Kim, JU Park, EY Joo… - Computer methods and …, 2019 - Elsevier
Abstract Background and Objective This study demonstrates deep learning approaches with
an aim to find the optimal method to automatically detect sleep apnea (SA) events from an …
an aim to find the optimal method to automatically detect sleep apnea (SA) events from an …
Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals
Sleep apnea is a common sleep disorder that causes repeated breathing interruption during
sleep. The performance of automated apnea detection methods based on respiratory …
sleep. The performance of automated apnea detection methods based on respiratory …
Detection of sleep apnea using deep neural networks and single-lead ECG signals
Sleep apnea causes frequent cessation of breathing during sleep. Feature extraction
approaches play a key role in the performance of apnea detection algorithms that use single …
approaches play a key role in the performance of apnea detection algorithms that use single …
Attention-based LSTM for non-contact sleep stage classification using IR-UWB radar
Manual scoring of sleep stages from polysomnography (PSG) records is essential to
understand the sleep quality and architecture. Since the PSG requires specialized …
understand the sleep quality and architecture. Since the PSG requires specialized …
Automatic detection of obstructive sleep apnea events using a deep CNN‐LSTM model
J Zhang, Z Tang, J Gao, L Lin, Z Liu… - Computational …, 2021 - Wiley Online Library
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 …
world, more and more people are suffering from OSA. Because of the limitation of monitor …
A convolutional neural network architecture to enhance oximetry ability to diagnose pediatric obstructive sleep apnea
F Vaquerizo-Villar, D Álvarez… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
This study aims at assessing the usefulness of deep learning to enhance the diagnostic
ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea …
ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea …
A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
The gold standard approach to diagnose obstructive sleep apnea (OSA) in children is
overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous …
overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous …
Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals
A novel framework for automatic detection of obstructive sleep apnea (OSA) is introduced in
which a symbolic dynamics method, alphabet entropy, along with other well-known features …
which a symbolic dynamics method, alphabet entropy, along with other well-known features …