AIMC Topic: Polysomnography

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Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study.

Journal of medical Internet research
BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time consuming and labor intensive, thus limiting di...

Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.

Computers in biology and medicine
Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as the primary research method. Conventional polysomnography primarily relies on cerebral electroencep...

Synergic Integration of the miRNome, Machine Learning and Bioinformatics for the Identification of Potential Disease-Modifying Agents in Obstructive Sleep Apnea.

Archivos de bronconeumologia
INTRODUCTION: Understanding the diverse pathogenetic pathways in obstructive sleep apnea (OSA) is crucial for improving outcomes. microRNA (miRNA) profiling is a promising strategy for elucidating these mechanisms.

SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.

Sensors (Basel, Switzerland)
Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS i...

Exploring the complexity of obstructive sleep apnea: findings from machine learning on diagnosis and predictive capacity of individual factors.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnoea (OSA) is a prevalent sleep disorder characterized by pharyngeal airway collapse during sleep, leading to intermittent hypoxia, intrathoracic pressure swings, and sleep fragmentation. OSA is associated with various co...

ESSN: An Efficient Sleep Sequence Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics
By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, exis...

CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics
Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signal...

Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Conventional obstructive sleep apnea (OSA) diagnosis via polysomnography can be costly and inaccessible. Recent advances in artificial intelligence (AI) have enabled the use of craniofacial photographs to diagnose OSA. This meta-analysis aim...

Assessment of simulated snoring sounds with artificial intelligence for the diagnosis of obstructive sleep apnea.

Sleep medicine
BACKGROUND: Performing simulated snoring (SS) is a commonly used method to evaluate the source of snoring in obstructive sleep apnea (OSA). SS sounds is considered as a potential biomarker for OSA. SS sounds can be easily recorded, which is a cost-ef...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional r...