AIMC Topic: Polysomnography

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Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Journal of neuroscience methods
BACKGROUND: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are prom...

Real-time prediction of disordered breathing events in people with obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Conventional therapies for obstructive sleep apnea (OSA) are effective but suffer from poor patient adherence and may not fully alleviate major OSA-associated cardiovascular risk factors or improve certain aspects of quality of life. Predict...

The Hypno-PC: uncovering sleep dynamics through principal component analysis and hidden Markov modeling of electrophysiological signals.

Sleep
Manual sleep scoring segments sleep into discrete 30-s epochs (wake, non-rapid-eye-movement [NREM] 1-3, rapid-eye-movement [REM]), yet substantial evidence suggests that sleep unfolds as a continuous, microstate-rich process. Using a data-driven appr...

DistillSleep: real-time, on-device, interpretable sleep staging from single-channel electroencephalogram.

Sleep
STUDY OBJECTIVES: Polysomnography (PSG) is the current gold standard for sleep staging but requires laboratory equipment, multiple sensors, and labor-intensive manual scoring. We developed DistillSleep, a single-channel electroencephalogram (EEG) fra...

A comparative analysis of automatic and manual scoring methods in polysomnography.

Sleep
The objective of this study was to compare twenty-six polysomnography (PSG) parameters between the groups utilizing automatic scoring (AS) software and manual scoring (MS) technique. Two MS groups, each comprising technicians with sleep-scoring exper...

Multi-View Self-Supervised Learning Enhances Automatic Sleep Staging From EEG Signals.

IEEE transactions on bio-medical engineering
Deep learning-based methods for automatic sleep staging offer an efficient and objective alternative to costly manual scoring. However, their reliance on extensive labeled datasets and the challenge of generalization to new subjects and datasets limi...

Incorporating respiratory signals for machine learning-based multimodal sleep stage classification: a large-scale benchmark study with actigraphy and heart rate variability.

Sleep
Insufficient sleep quality is directly linked to various diseases, making reliable sleep monitoring crucial for prevention, diagnosis, and treatment. As sleep laboratories are cost- and resource-prohibitive, wearable sensors offer a promising alterna...

Artificial intelligence or sleep experts: comparing polysomnographic sleep staging in children and adolescents.

Sleep
STUDY OBJECTIVES: The manual annotation of polysomnography (PSG) hypnograms is difficult and time-consuming. U-Sleep is an alternative, fast, and publicly available, automated sleep staging system evaluated in adult PSGs. In this study, we compare th...

Ultra-low-power System-on-Chip for automated screening of central apnea and hypopnea via chin electromyography.

Computers in biology and medicine
Central Apnea (CA) and Central Hypopnea (CH) are sleep disorders arising from the brain's inability to signal respiratory muscles, potentially leading to severe complications such as heart failure. This study presents a novel system for automating CA...

Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes.

ACS sensors
The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and accessible sleep monitoring solutions. Polysomnography (PSG) remains the clinical gold standard for dia...