AIMC Topic: Sleep Apnea Syndromes

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

Cardiovascular engineering and technology Mar 17, 2022
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.
Deep Learning Electrocardiography Sleep Apnea Syndromes Humans Neural Networks, Computer Algorithms
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Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

IEEE journal of biomedical and health informatics Nov 5, 2021
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...
Sleep Apnea Syndromes Sleep Apnea, Obstructive Polysomnography Humans Deep Learning Canada
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Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.

Sensors (Basel, Switzerland) Aug 11, 2021
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...
Aged Deep Learning Electrocardiography Sleep Apnea Syndromes Humans Sleep Apnea, Obstructive Neural Networks, Computer Polysomnography
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ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome.

Journal of healthcare engineering Jul 7, 2021
This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic ...
Humans Neural Networks, Computer Incidence Electrocardiography Bayes Theorem Sleep Apnea Syndromes
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Screening of sleep apnea based on heart rate variability and long short-term memory.

Sleep & breathing = Schlaf & Atmung Jan 10, 2021
PURPOSE: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a c...
Female Aged Aged, 80 and over Humans Neural Networks, Computer Machine Learning Male Middle Aged Adolescent Young Adult Adult Sensitivity and Specificity Autonomic Nervous System Electrocardiography Sleep Apnea Syndromes Heart Rate
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Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

Journal of Korean medical science Dec 7, 2020
BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.
Electrocardiography Signal Processing, Computer-Assisted Sleep Apnea Syndromes Middle Aged Severity of Illness Index Deep Learning Humans Aged Female Adult Male
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Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning.

Sensors (Basel, Switzerland) Nov 13, 2020
Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical signifi...
Male Deep Learning Wearable Electronic Devices Sleep Apnea Syndromes Sleep Apnea, Obstructive Respiration Humans Female Adult
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Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

Physiological measurement Nov 6, 2020
OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.
Polysomnography Electrocardiography Humans Sleep Apnea Syndromes Machine Learning Atrial Fibrillation Risk Factors
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Greedy based convolutional neural network optimization for detecting apnea.

Computer methods and programs in biomedicine Jul 4, 2020
BACKGROUND AND OBJECTIVE: Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be develope...
Electrocardiography Humans Sleep Apnea Syndromes Neural Networks, Computer Polysomnography Databases, Factual
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Predicting polysomnographic severity thresholds in children using machine learning.

Pediatric research May 9, 2020
BACKGROUND: Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breathing (oSDB). Although polysomnography is beneficial for preoperative risk stratification in these chi...
Risk Actigraphy Hypersensitivity Surveys and Questionnaires Sleep Apnea Syndromes Polysomnography Female Child Sleep Apnea, Obstructive Decision Making Humans Oximetry Asthma Monitoring, Physiologic Tonsillectomy Machine Learning Anthropometry Adenoidectomy Male Algorithms Signal Processing, Computer-Assisted
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