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 …
Review of deep learning-based atrial fibrillation detection studies
Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and
premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming …
premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming …
Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network
Q Yang, L Zou, K Wei, G Liu - Computers in biology and medicine, 2022 - Elsevier
Obstructive sleep apnea (OSA), which has high morbidity and complications, is diagnosed
via polysomnography (PSG). However, this method is expensive, time-consuming, and …
via polysomnography (PSG). However, this method is expensive, time-consuming, and …
[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
[HTML][HTML] An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
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 …
TFCNN-BiGRU with self-attention mechanism for automatic human emotion recognition using multi-channel EEG data
Electroencephalograms (EEG)-based technology for recognizing emotions has attracted a
lot of interest lately. However, there is still work to be done on the efficient fusion of different …
lot of interest lately. However, there is still work to be done on the efficient fusion of different …
BCI-based consumers' choice prediction from EEG signals: an intelligent neuromarketing framework
Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how
customers react to marketing stimuli. Marketers spend about $750 billion annually on …
customers react to marketing stimuli. Marketers spend about $750 billion annually on …
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 …
Application of artificial intelligence in the diagnosis of sleep apnea
G Bazoukis, SC Bollepalli, CT Chung, X Li… - Journal of Clinical …, 2023 - jcsm.aasm.org
Study Objectives: Machine learning (ML) models have been employed in the setting of sleep
disorders. This review aims to summarize the existing data about the role of ML techniques …
disorders. This review aims to summarize the existing data about the role of ML techniques …