AIMC Topic: Sleep Apnea Syndromes

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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 Sleep Apnea Syndromes Polysomnography Humans Neural Networks, Computer 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...
Humans Machine Learning Algorithms Polysomnography Decision Making Surveys and Questionnaires Oximetry Tonsillectomy Child Signal Processing, Computer-Assisted Risk Adenoidectomy Female Sleep Apnea Syndromes Monitoring, Physiologic Actigraphy Asthma Hypersensitivity Sleep Apnea, Obstructive Male Anthropometry
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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...
Humans Deep Learning Sleep Apnea Syndromes Electric Impedance Respiratory Rate Polysomnography
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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...
Neural Networks, Computer Deep Learning Sleep Apnea Syndromes Humans
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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...
Male Electroencephalography Sleep Apnea Syndromes Electrooculography Sleep Stages Databases, Factual Female Adult Humans Neural Networks, Computer
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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...
Adult Oximetry Middle Aged Oxygen Signal Processing, Computer-Assisted Sleep Apnea Syndromes Sleep Apnea, Obstructive Humans Neural Networks, Computer Aged
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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.
Humans Databases, Factual Deep Learning Electrocardiography Sleep Apnea Syndromes Automation
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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...
Sleep Apnea Syndromes Respiration Polysomnography Electrocardiography Support Vector Machine Female Sensitivity and Specificity Signal Processing, Computer-Assisted Reproducibility of Results Male Adult Middle Aged Humans Neural Networks, Computer Algorithms
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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...
Respiration Signal Processing, Computer-Assisted Sleep Apnea Syndromes Heart Rate Humans Electrocardiography Support Vector Machine
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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...
Sleep Apnea, Obstructive Snoring Polysomnography Middle Aged Severity of Illness Index Sleep Apnea Syndromes Algorithms Humans Female Machine Learning Male
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