Latest AI and machine learning research in sleep disorders for healthcare professionals.
This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programme (ITP). A key aim was to identify individuals likely to benefit from additional, sleep-focused interventions. A total of 449 veterans completed the I...
Manual scoring of polysomnography (PSG) is a time-intensive task, prone to inter-scorer variability that can impact diagnostic reliability. This study investigates the integration of decision support systems (DSS) into PSG scoring workflows, focusing on their effects on accuracy, scoring time and potential biases toward recommendations from artificial intelligence (AI) compared to human-generated ...
INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as quest...
A machine-learning (ML) based model that is capable of predicting the formation free energy of a silicic acid oligomer (OSA) with its SMILES string wa...
OBJECTIVE: This study compared the brain function changes in chronic insomnia disorder (CID) before and after treatment by suanzaoren decoction (SZRD)...
BACKGROUND: Insomnia, a common mental health issue, is characterized by brain hyperarousal and difficulties in transitioning between sleep stages. Pha...
Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly an...
Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocognitive risk factors. Current multi-sensor diagnostic...
The objective of this study was to compare twenty-six polysomnography (PSG) parameters between the groups utilizing automatic scoring (AS) software an...
Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt n...
Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating patterns (CAP) can be observed in EEG signals duri...
Parasomnias are abnormal behaviours or mental experiences during sleep or the sleep-wake transition. As disorders of arousal (DOA) or REM sleep behavi...
Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. A...
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is one of the most common sleep disorders affecting nearly one billion of the global adult populatio...
In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction wi...
Narcolepsy is a chronic neurodegenerative disorder defined by the selective loss of orexin-producing neurons in the lateral hypothalamus, leading to e...
Conventional sleep staging categorises sleep into discrete stages, which may not capture the continuous nature of sleep depth. We aimed to develop a d...
Obstructive Sleep Apnea (OSA) is a common disorder characterized by repeated airway collapse during sleep, leading to significant health risks. The tr...
PURPOSE: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polyso...
Noise is a major global environmental issue that raises concerns about both mental and physical health. However, few studies have investigated the med...