BACKGROUND: Various deep-learning systems have been proposed for automated sleep staging. Still, the significance of age-specific underrepresentation in training data and the resulting errors in clinically used sleep metrics are unknown.
BACKGROUND: Treatment of obstructive sleep apnea is crucial for long term health and reduced economic burden. For those considered for surgery, drug-induced sleep endoscopy (DISE) is a method to characterize location and pattern of sleep-related uppe...
Classifying sleep stages in real-time represents considerable potential, for instance in enabling interactive noise masking in noisy environments when persons are in a state of light sleep or to support clinical staff in analyzing sleep patterns etc....
BACKGROUND: The main classification systems of sleep disorders are based on the subjective self-reported criteria. Objective measures are essential to characterize the nocturnal sleep disturbance, identify daytime impairment, and determine the course...
BACKGROUND: Obstructive sleep apnea (OSA) remains massively underdiagnosed, due to limited access to polysomnography (PSG), the highly complex gold standard for diagnosis. Performance scores in predicting OSA are evaluated for machine learning (ML) a...
OBJECTIVE: This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.
BACKGROUND: Estimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores.
OBJECTIVE: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic LMs (PLMS) in overnight polysomnography (PSG) studies, which is subject to inter-scorer variability. The objective of this study is to design and val...
OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction.
BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI).