Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilit...
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 ...
Actigraphy, a tool known for investigating sleep-wake patterns at home, lacks scientific validation in hypersomnolent subjects. We aim to validate an actigraphy-based sleep-wake prediction algorithm against 32-h continuous polysomnography in patients...
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...
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...
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...
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...
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...
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 ...
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...
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