AIMC Topic: Sleep Stages

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Complex-valued unsupervised convolutional neural networks for sleep stage classification.

Computer methods and programs in biomedicine Jul 26, 2018
BACKGROUND AND OBJECTIVE: Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be...
Algorithms Databases, Factual Female Aged Humans Neural Networks, Computer Wearable Electronic Devices Unsupervised Machine Learning Electroencephalography Sleep Stages Adult Male Middle Aged Deep Learning
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A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jan 31, 2018
OBJECTIVE: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resour...
Sleep Stages Humans Machine Learning Time Factors Electroencephalography Polysomnography
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A New Method for Automatic Sleep Stage Classification.

IEEE transactions on biomedical circuits and systems Aug 14, 2017
Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for autom...
Neural Networks, Computer Electroencephalography Humans Signal Processing, Computer-Assisted Machine Learning Sleep Stages Algorithms Models, Theoretical
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Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification.

Computers in biology and medicine Aug 8, 2017
The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respecti...
Humans Female Wrist Heart Rate Thorax Actigraphy Adult Male Middle Aged Support Vector Machine Polysomnography 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...
Sleep Stages Forehead Electrodes Reference Standards Electrooculography Humans Neural Networks, Computer Machine Learning Algorithms Reproducibility of Results Electroencephalography Equipment Design
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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 ...
Narcolepsy Polysomnography Electrooculography Sleep Stages Electroencephalography Electromyography Linear Models Humans Machine Learning Adult Sensitivity and Specificity Middle Aged Cohort Studies Discriminant Analysis
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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...
Humans Machine Learning Female Adult Electroencephalography Sleep Stages Signal Processing, Computer-Assisted Male Sleep Initiation and Maintenance Disorders Middle Aged Polysomnography Diagnosis, Computer-Assisted
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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 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...
Electroencephalography Pattern Recognition, Automated Polysomnography Sleep Stages Diagnosis, Computer-Assisted Humans Computer Simulation Models, Statistical Machine Learning Female Data Interpretation, Statistical Algorithms Reproducibility of Results Bayes Theorem Adult Young Adult Sensitivity and Specificity Male
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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...
Mice Electroencephalography Signal Processing, Computer-Assisted Electromyography Sleep Stages Machine Learning Algorithms Animals
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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.
Fuzzy Logic Electromyography Humans Young Adult Electroencephalography Adult Sleep Initiation and Maintenance Disorders Algorithms Signal Processing, Computer-Assisted Male Polysomnography Female Adolescent Sleep Stages
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