AIMC Topic: Polysomnography

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Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans.

Sleep medicine
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...

Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.

Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale
BACKGROUND: Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, req...

Polysomnographic identification of anxiety and depression using deep learning.

Journal of psychiatric research
Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conserved health state. Anxiety and depression have a bidirectional relationship with sleep. This study re...

Detecting obstructive sleep apnea by craniofacial image-based deep learning.

Sleep & breathing = Schlaf & Atmung
STUDY OBJECTIVES: This study aimed to develop a deep learning-based model to detect obstructive sleep apnea (OSA) using craniofacial photographs.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

IEEE journal of biomedical and health informatics
Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data f...

Inter-database validation of a deep learning approach for automatic sleep scoring.

PloS one
STUDY OBJECTIVES: Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restr...

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.

Sensors (Basel, Switzerland)
Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obst...

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

IEEE journal of biomedical and health informatics
Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional s...

Cross-Gram matrices and their use in transfer learning: Application to automatic REM detection using heart rate.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years t...

Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.

Sleep medicine
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.