AIMC Topic: Polysomnography
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Of Pilots and Copilots: The Evolving Role of Artificial Intelligence in Clinical Neurophysiology.
The Neurodiagnostic journal
Feb 25, 2025
Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), electromyography (EMG), and polysomnography (PSG). AI enhances diagnostic accuracy and efficiency while ...
Robotic beds for the treatment of positional obstructive sleep apnea - A randomized cross-over pilot trial.
Sleep medicine
Feb 20, 2025
BACKGROUND: Interventions leading to avoidance of supine position and thus reducing the likelihood of upper airway collapse during sleep are a treatment approach for positional obstructive sleep apnea (POSA). The aim of this randomized cross-over tri...
WaveSleepNet: An Interpretable Network for Expert-Like Sleep Staging.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Although deep learning algorithms have proven their efficiency in automatic sleep staging, their "black-box" nature has limited their clinical adoption. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that re...
SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagno...
Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.
Computers in biology and medicine
Feb 8, 2025
Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. However, existing approaches such as polysomnography, considered the most reliable and accurate test to de...
S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models.
Computers in biology and medicine
Feb 3, 2025
Machine-learning-based automatic sleep stage scoring is a promising approach to enhance the time-consuming manual annotation process of polysomnography recordings. Although numerous algorithms have been proposed for this purpose, systematic explorati...
Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.
Sleep & breathing = Schlaf & Atmung
Feb 1, 2025
PURPOSE: Despite increased awareness of sleep hygiene, over 80% of sleep apnea cases remain undiagnosed, underscoring the need for accessible screening methods. This study presents a method for detecting sleep apnea using data from the Apple Watch's ...
Deep Learning for Pediatric Sleep Staging From Photoplethysmography: A Transfer Learning Approach From Adults to Children.
IEEE transactions on bio-medical engineering
Jan 21, 2025
BACKGROUND: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have sh...
Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea.
BMC neuroscience
Dec 31, 2024
Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome...
Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Dec 24, 2024
BACKGROUND: Obstructive sleep apnea (OSA) is common after stroke. Still, routine screening of OSA with polysomnography (PSG) is often unfeasible in clinical practice, primarily because of how limited resources are and the physical condition of patien...
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