AIMC Topic: Sleep Stages

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Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines.

NeuroImage
A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decodin...

Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders.

Annals of biomedical engineering
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine m...

Sleep stage classification with ECG and respiratory effort.

Physiological measurement
Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that a...

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

Journal of neuroscience methods
BACKGROUND: Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state...

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...

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...

Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation.

IEEE transactions on bio-medical engineering
Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: ...

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...