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 …
Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review
Objective: Sleep apnea (SA), a common sleep disorder, can significantly decrease the
quality of life, and is closely associated with major health risks such as cardiovascular …
quality of life, and is closely associated with major health risks such as cardiovascular …
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 …
Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network
In this study, we propose a method for the automated detection of obstructive sleep apnea
(OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network …
(OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network …
Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram
E Urtnasan, JU Park, KJ Lee - Physiological measurement, 2018 - iopscience.iop.org
Objective: In this paper, we propose a convolutional neural network (CNN)-based deep
learning architecture for multiclass classification of obstructive sleep apnea and hypopnea …
learning architecture for multiclass classification of obstructive sleep apnea and hypopnea …
Sleep apnea: a review of diagnostic sensors, algorithms, and therapies
M Shokoueinejad, C Fernandez, E Carroll… - Physiological …, 2017 - iopscience.iop.org
While public awareness of sleep related disorders is growing, sleep apnea syndrome (SAS)
remains a public health and economic challenge. Over the last two decades, extensive …
remains a public health and economic challenge. Over the last two decades, extensive …
Obstructive sleep apnea event prediction using recurrence plots and convolutional neural networks (RP-CNNs) from polysomnographic signals
Y Taghizadegan, NJ Dabanloo, K Maghooli… - … Signal Processing and …, 2021 - Elsevier
Abstract The prediction of Obstructive Sleep Apnea (OSA) through common
polysomnographic signals before stop breathing triggers the ventilation-aided machines …
polysomnographic signals before stop breathing triggers the ventilation-aided machines …
A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal
R Ranjan, R Arya, SL Fernandes, E Sravya… - Pattern Recognition …, 2018 - Elsevier
The study of sleep stages and the associated signals have emerged as a very important
parameter to identify the neurological disorders and test of mental activities nowadays …
parameter to identify the neurological disorders and test of mental activities nowadays …
Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review
Background and objective Sleep apnea syndrome (SAS), which can significantly decrease
the quality of life is associated with a major risk factor of health implications such as …
the quality of life is associated with a major risk factor of health implications such as …
Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine
J Zhou, X Wu, W Zeng - Journal of clinical monitoring and computing, 2015 - Springer
Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many
studies focus on using simple and efficient methods for SAS detection instead of …
studies focus on using simple and efficient methods for SAS detection instead of …