AIMC Topic: Sleep Stages
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Mixed Neural Network Approach for Temporal Sleep Stage Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 28, 2017
This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monit...
Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy.
Journal of neuroscience methods
Feb 20, 2017
BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized ...
Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis.
IEEE journal of biomedical and health informatics
Jan 9, 2017
Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channe...
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
Oct 19, 2016
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...
Data Interpretation, Statistical
Bayes Theorem
Electroencephalography
Pattern Recognition, Automated
Sensitivity and Specificity
Male
Diagnosis, Computer-Assisted
Models, Statistical
Polysomnography
Sleep Stages
Humans
Machine Learning
Algorithms
Computer Simulation
Female
Reproducibility of Results
Young Adult
Adult
Multiple classifier systems for automatic sleep scoring in mice.
Journal of neuroscience methods
Feb 27, 2016
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...
Combination of Expert Knowledge and a Genetic Fuzzy Inference System for Automatic Sleep Staging.
IEEE transactions on bio-medical engineering
Dec 18, 2015
OBJECTIVE: In this paper, the genetic fuzzy inference system based on expert knowledge for automatic sleep staging was developed.
The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness.
Accident; analysis and prevention
Dec 10, 2015
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
Oct 24, 2015
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
Oct 13, 2015
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
Aug 19, 2015
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...
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