AIMC Topic: Sleep Apnea, Central

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Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks.

Sleep medicine Mar 29, 2025
Sleep apnea diagnosis relies on polysomnography (PSG), which is resource-intensive and requires manual analysis to differentiate obstructive sleep apnea (OSA) from central sleep apnea (CSA). Existing portable devices, while valuable in detecting slee...
Humans Neural Networks, Computer Female Adult Sensitivity and Specificity Male Middle Aged Deep Learning Sleep Apnea, Obstructive Polysomnography Respiratory Sounds Sleep Apnea, Central
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Revolutionizing congenital central hypoventilation syndrome screening: the potential of machine learning approaches.

Pediatric research Feb 10, 2024
Humans Machine Learning Infant, Newborn Neonatal Screening Sleep Apnea, Central Hypoventilation
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Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

Journal of medical Internet research May 22, 2020
BACKGROUND: Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apn...
Humans Female Male Middle Aged Deep Learning Sleep Apnea, Obstructive Spectrophotometry, Infrared Polysomnography Sleep Apnea, Central
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Ultra-low-power System-on-Chip for automated screening of central apnea and hypopnea via chin electromyography.

Computers in biology and medicine Jul 1, 2025
Central Apnea (CA) and Central Hypopnea (CH) are sleep disorders arising from the brain's inability to signal respiratory muscles, potentially leading to severe complications such as heart failure. This study presents a novel system for automating CA...
Humans Neural Networks, Computer Female Male Support Vector Machine Signal Processing, Computer-Assisted Electromyography Polysomnography Chin Sleep Apnea, Central
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