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Sleep Stages

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Bayesian Nonnegative CP Decomposition-Based Feature Extraction Algorithm for Drowsiness Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Daytime short nap involves physiological processes, such as alertness, drowsiness and sleep. The study of the relationship between drowsiness and nap based on physiological signals is a great way to have a better understanding of the periodical rhyme...

Multiple classifier systems for automatic sleep scoring in mice.

Journal of neuroscience methods
BACKGROUND: Electroencephalogram (EEG) and electromyogram (EMG) recordings are often used in rodents to study sleep architecture and sleep-associated neural activity. These recordings must be scored to designate what sleep/wake state the animal is in...

The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness.

Accident; analysis and prevention
Slowed eyelid closure coupled with increased duration and frequency of closure is associated with drowsiness. This study assessed the utility of two devices for automated measurement of slow eyelid closure in a standard poor performance condition (al...

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...

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Sleep
STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.