AIMC Topic: Sleep Apnea Syndromes

Clear Filters Showing 31 to 40 of 89 articles

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Physical and engineering sciences in medicine
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive...

Sleep Apnea Prediction Using Deep Learning.

IEEE journal of biomedical and health informatics
Obstructive sleep apnea (OSA) is a sleep disorder that causes partial or complete cessation of breathing during an individual's sleep. Various methods have been proposed to automatically detect OSA events, but little work has focused on predicting su...

Deep learning technique to detect craniofacial anatomical abnormalities concentrated on middle and anterior of face in patients with sleep apnea.

Sleep medicine
OBJECTIVES: The aim of this study is to propose a deep learning-based model using craniofacial photographs for automatic obstructive sleep apnea (OSA) detection and to perform design explainability tests to investigate important craniofacial regions ...

SelANet: decision-assisting selective sleep apnea detection based on confidence score.

BMC medical informatics and decision making
BACKGROUND: One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to devel...

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea.

Computers in biology and medicine
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. Accordingly, we aimed to develop an accurate and interpretable deep...

A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Physiological measurement
Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone...

Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

Methods (San Diego, Calif.)
The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. Recent studies have shown that radar-based non-contact vital sign monitoring system can effectively d...

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG.

Cardiovascular engineering and technology
OBJECTIVES: Sleep apnea is the most common sleep disorder that leads to serious health complications if not treated early. Forecasting apnea occurrence ahead in time provides the opportunity to take appropriate actions to control and manage it.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

IEEE journal of biomedical and health informatics
Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data f...

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.

Sensors (Basel, Switzerland)
Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obst...