AIMC Topic: Polysomnography

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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 ...

Generation of a Free-Living Ground-Truth Validation Dataset for Wearable Measures of Physical Activity, Sedentary Behavior, Sleep, and Heart Rate in Adults (OxWEARS): Protocol for a Cross-Sectional Study.

JMIR research protocols
BACKGROUND: Wearable devices enable continuous measurement of physical activity, sedentary behavior, sleep, and heart rate under free-living conditions. However, most validation studies rely on small, homogeneous samples; are conducted under laborato...

Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning.

Scientific reports
Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. Although polysomnography is the gold standard for arousal detection, its cost and complexity limit us...

AI-driven clinical decision support for early diagnosis and treatment planning in patients with suspected sleep apnea using clinical and demographic data before sleep studies.

NPJ primary care respiratory medicine
OBJECTIVE: This study explored the application of Machine Learning (ML) techniques to cluster patients with suspected sleep apnea (SA), based on clinical-demographic data, with the aim of optimizing diagnostic pathways and enabling more personalized ...

Tracing the evolution in sleep apnea detection: a review from traditional non-contact under-the-mattress devices to advanced AI-driven methods.

Sleep & breathing = Schlaf & Atmung
BACKGROUND: Sleep apnea is traditionally diagnosed with polysomnography (PSG), which, while effective, is costly, time-consuming, and obtrusive. Recent advancements in biosensing technologies have facilitated the development of under-the-mattress dev...

Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals.

Nature communications
Sleep disorders affect billions globally, yet diagnostic access remains limited by healthcare resource constraints. Here, we develop a deep learning framework that analyzes respiratory signals for remote sleep health monitoring, trained on 15,785 nig...

EEG based classification of sleep cyclic alternating patterns using frequency driven forward ternary encoding.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Cyclic alternating patterns (CAP) of sleep can be observed through electroencephalogram (EEG) signals. Analyzing CAP can provide valuable insights into different abnormalities relating to sleep. CAP comprises of two phases: A and B, characte...

Improved non-invasive detection of sleep stages when combining skin sympathetic nerve activity and heart rate variability analysis with AI.

Scientific reports
Sleep is a cyclic physiological process that goes into different stages, and every stage has its' importance in the construction or recovery of physiological function. Sleep scoring is performed from polysomnography recordings which requires signals ...

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-based estimation of the mild cognitive impairment stage using multimodal physical and behavioral measures.

Scientific reports
Mild cognitive impairment (MCI) is a prodromal stage of dementia, and its early detection is critical for improving clinical outcomes. However, current diagnostic tools such as brain magnetic resonance imaging (MRI) and neuropsychological testing hav...