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 …
[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications
Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of
partial or complete upper airway obstruction caused by narrowing or collapse of the …
partial or complete upper airway obstruction caused by narrowing or collapse of the …
Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals
Background and objective Epilepsy is a prevalent disorder that affects the central nervous
system, causing seizures. In the current study, a novel algorithm is developed using …
system, causing seizures. In the current study, a novel algorithm is developed using …
HARDC: A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Deep learning-based models have achieved significant success in detecting cardiac
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
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 …
SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals
Sleep apnea is a common symptomatic disease affecting nearly 1 billion people around the
world. The gold standard approach for determining the severity of sleep apnea is full-night …
world. The gold standard approach for determining the severity of sleep apnea is full-night …
New hybrid deep learning approach using BiGRU-BiLSTM and multilayered dilated CNN to detect arrhythmia
Deep learning methods have shown early progress in analyzing complicated ECG signals,
especially in heartbeat classification and arrhythmia detection. However, there is still a long …
especially in heartbeat classification and arrhythmia detection. However, there is still a long …
AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning
Abstract Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related
breathing disorder. It is caused by an increased upper airway resistance during sleep, which …
breathing disorder. It is caused by an increased upper airway resistance during sleep, which …
Classifier precision analysis for sleep apnea detection using ECG signals
This article presents a study on the efficiency of implementing classifiers for the detection of
sleep apnea moments based on a minute-to-minute Electrocardiogram (ECG) signal …
sleep apnea moments based on a minute-to-minute Electrocardiogram (ECG) signal …
Obstructive sleep apnea detection using discrete wavelet transform-based statistical features
Motivation and objective Obstructive sleep apnea (OSA) is a sleep disorder identified in
nearly 10% of middle-aged people, which deteriorates the normal functioning of human …
nearly 10% of middle-aged people, which deteriorates the normal functioning of human …