AIMC Topic: Sleep Apnea, Obstructive

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Development of an explainable prediction model for the risk of moderate-to-severe obstructive sleep apnea in children.

European journal of pediatrics
UNLABELLED: Early identification of children at high risk for moderate-to-severe obstructive sleep apnea (OSA) is crucial for timely intervention, yet is often hindered by limited access to polysomnography (PSG). We aimed to develop an interpretable ...

SleepPPG-Net2: deep learning generalization for sleep staging from photoplethysmography.

Physiological measurement
. sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent photoplethysmography (PPG)-based deep learning models perform well on local datasets but struggle...

Integrating bulk and single-cell RNA sequencing data to dissect genetic links between periodontitis and obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Periodontitis (PD) and obstructive sleep apnea (OSA) are widespread conditions with profound health consequences. Increasing evidence suggests shared pathophysiological mechanisms between PD and OSA, prompting this study to explore their gen...

Automated OSAHS detection from ECG using temporal convolutional network.

Scientific reports
Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a prevalent systemic disorder affecting approximately 1 billion people worldwide, associated with severe outcomes such as sudden death and traffic accidents. Despite its significant impact, OSAHS i...

Machine learning insights into obesity related genes XRCC4 and ARL6 in obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnea (OSA) is highly prevalent among obese individuals, with a complex and bidirectional relationship wherein obesity not only serves as a primary risk factor for OSA but also exacerbates its severity. This interconnection...

Investigating the role of depression in obstructive sleep apnea and predicting risk factors for OSA in depressed patients: machine learning-assisted evidence from NHANES.

BMC psychiatry
OBJECTIVE: The relationship between depression and obstructive sleep apnea (OSA) remains controversial. Therefore, this study aims to explore their association and utilize machine learning models to predict OSA among individuals with depression withi...

Electrocardiogram heart rate variability for machine learning diagnosis of obstructive sleep Apnoea: A bayesian meta-analysis.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnoea syndrome (OSA) is a common yet underdiagnosed condition associated with significant health risks. Although polysomnography is the diagnostic gold standard, it is resource-intensive and unsuitable for widespread scree...

Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.

Sleep & breathing = Schlaf & Atmung
PURPOSE: obstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal, an application recording sound and movements thanks to a smartphone's microphone, accelerometer and gyro...

AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.

PloS one
With an increasing aging population, the prevalence of chronic comorbidities is on the rise. The potential relationship between obstructive sleep apnea (OSA) and osteoporosis has garnered significant attention. Most studies examining the association ...

Belun Sleep Platform versus in-lab polysomnography for obstructive sleep apnea diagnosis.

Sleep & breathing = Schlaf & Atmung
OBJECTIVE: We aimed to compare the Belun Sleep Platform (BSP), an artificial intelligence-driven home sleep testing device, with polysomnography (PSG) for diagnosing obstructive sleep apnea. The BSP analyzes oxygen saturation, heart rate, and acceler...