AIMC Topic: Polysomnography

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Artificial intelligence based platform for the automatic and simultaneous explainable detection of apnoea, oxygen desaturation, and artefacts in paediatric polygraphy exams (REST).

Scientific reports
The gold standard for the diagnosis of sleep apnoea (SA) is polysomnography, consisting of overnight in-lab tests, which are expensive for both patients and healthcare systems. Airflow and pulse/oximetry signals contain most of the necessary informat...

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...

Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet.

Scientific reports
Sleep apnea, a prevalent respiratory disorder, poses significant health risks, including cardiovascular complications and behavioral issues, if left untreated. Traditional diagnostic methods like polysomnography, although effective, are often expensi...

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...

A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.

Scientific data
Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complications, including hypertension, arrhythmias, cognitive impair- ment, and metabolic disturbances. Due to the insidious nature of its symptoms, patients oft...

Beyond accuracy: a framework for evaluating algorithmic bias and performance, applied to automated sleep scoring.

Scientific reports
Recent advancements in artificial intelligence (AI) have significantly improved sleep-scoring algorithms, bringing their performance close to the theoretical limit of approximately 80%, which aligns with inter-scorer agreement levels. While this sugg...

Automated sleep staging model for older adults based on CWT and deep learning.

Scientific reports
Sleep staging plays a crucial role in the diagnosis and treatment of sleep disorders. Traditional sleep staging requires manual classification by professional technicians based on the characteristic features of each sleep stage. This process is time-...

Patch-type wearable electrocardiography and impedance pneumography for sleep staging: A multi-modal deep learning approach.

Computers in biology and medicine
Sleep staging is critical for investigating sleep quality and detecting disorders. Polysomnography (PSG) remains the gold standard, but is costly and impractical for routine monitoring. This study evaluates the feasibility of a patch-type wearable de...

Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.

PloS one
INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This high...