AIMC Topic: Sleep Apnea Syndromes
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Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.
IEEE journal of biomedical and health informatics
Jan 20, 2020
Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting t...
A Systematic Review of Detecting Sleep Apnea Using Deep Learning.
Sensors (Basel, Switzerland)
Nov 12, 2019
Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and imple...
A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.
Journal of neural engineering
Oct 29, 2019
OBJECTIVE: Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electroencephalogram (EEG) signals perform well, acquiring EEG signals is complicated and uncomfortable; t...
Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea.
Scientific reports
Sep 13, 2019
The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for t...
Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram.
Computer methods and programs in biomedicine
Jul 30, 2019
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 electrocardiogram (ECG) signal.
A RR interval based automated apnea detection approach using residual network.
Computer methods and programs in biomedicine
May 8, 2019
BACKGROUND AND OBJECTIVE: Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to th...
Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals.
Computers in biology and medicine
Mar 26, 2019
Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived res...
Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Mar 15, 2019
STUDY OBJECTIVES: Snoring is perceived to be directly proportional to sleep apnea severity, especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and objectively evaluated, despite its popularity in clinical practice. This...
Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.
Physiological measurement
Feb 26, 2019
OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P ) monitoring is the gold standard for measuring respiratory effort, but it is typically po...
An Intelligent Sleep Apnea Classification System Based on EEG Signals.
Journal of medical systems
Jan 8, 2019
Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the...
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