A review of automated sleep disorder detection
Automated sleep disorder detection is challenging because physiological symptoms can
vary widely. These variations make it difficult to create effective sleep disorder detection …
vary widely. These variations make it difficult to create effective sleep disorder detection …
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 …
Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review
Sleep apnea (SA) is a common sleep disorder that is not easy to detect. Recent studies have
highlighted ECG analysis as an effective method of diagnosing SA. Because the changes …
highlighted ECG analysis as an effective method of diagnosing SA. Because the changes …
Detection of apnea events from ECG segments using Fourier decomposition method
Absence of airflow in breathing during sleep for more than 10 s is known as sleep apnea. It
causes low oxygen levels in the blood which may lead to many cardiovascular problems …
causes low oxygen levels in the blood which may lead to many cardiovascular problems …
Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework
RK Tripathy, UR Acharya - Biocybernetics and Biomedical Engineering, 2018 - Elsevier
Sleep is a physiological activity and human body restores itself from various diseases during
sleep. It is necessary to get sufficient amount of sleep to have sound physiological and …
sleep. It is necessary to get sufficient amount of sleep to have sound physiological and …
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 …
Accurate detection of sleep apnea with long short-term memory network based on RR interval signals
Sleep apnea is a common condition that is characterized by sleep-disordered breathing.
Worldwide the number of apnea cases has increased and there has been a growing number …
Worldwide the number of apnea cases has increased and there has been a growing number …
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 …
Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis
The heartbeat interval (HBI) signal (RR-time series), and electrocardiogram (ECG) derived
respiration (EDR) signal quantify the information about the cardiopulmonary activity, and …
respiration (EDR) signal quantify the information about the cardiopulmonary activity, and …