AIMC Topic: Sleep Stages

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Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.

Physiological measurement Dec 21, 2018
OBJECTIVE: This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN).
Deep Learning Time Factors Electrocardiography Support Vector Machine Humans Neural Networks, Computer Signal Processing, Computer-Assisted Wakefulness Sleep Stages
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Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Nature communications Dec 6, 2018
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abn...
HLA-DQ beta-Chains Narcolepsy Polysomnography Aged Sleep Stages Young Adult Male Adult Humans Middle Aged Neural Networks, Computer Sensitivity and Specificity Adolescent Algorithms Cohort Studies Female
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Prediction error connectivity: A new method for EEG state analysis.

NeuroImage Dec 1, 2018
Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency domain, like spectral coherence. Considering brain interareal communication as binary interactions,...
Humans Adult Male Signal Processing, Computer-Assisted Cerebral Cortex Connectome Sleep Stages Support Vector Machine Models, Neurological Electroencephalography Epilepsy
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Detecting abnormal electroencephalograms using deep convolutional networks.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Nov 17, 2018
OBJECTIVES: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of...
Young Adult Adult Male Middle Aged Adolescent Electroencephalography Sleep Stages Machine Learning Databases, Factual Retrospective Studies Female Humans Neural Networks, Computer
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Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Journal of sleep research Nov 13, 2018
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here...
Adult Male Electroencephalography Actigraphy Humans Machine Learning Female Sleep Wake Disorders Sleep Stages
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Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

IEEE transactions on bio-medical engineering Oct 22, 2018
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequent...
Aged Young Adult Adult Male Middle Aged Polysomnography Humans Adolescent Sleep Stages Neural Networks, Computer Electrodiagnosis Signal Processing, Computer-Assisted Female
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Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device.

Computers in biology and medicine Oct 15, 2018
BACKGROUND: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wri...
Sleep Stages Adult Male Middle Aged Wearable Electronic Devices Signal Processing, Computer-Assisted Wrist Heart Rate Actigraphy Humans Neural Networks, Computer Female Young Adult
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EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Computational and mathematical methods in medicine Sep 4, 2018
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of trad...
Automation Male Biological Ontologies Humans Electroencephalography Sleep Stages Data Analysis Sleep Female Artifacts
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Quiet sleep detection in preterm infants using deep convolutional neural networks.

Journal of neural engineering Aug 22, 2018
OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analy...
Electroencephalography Female Humans Infant, Premature Wakefulness Infant, Newborn Sleep Stages Neural Networks, Computer Sleep Male Algorithms Automation Brain
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Modeling brain dynamic state changes with adaptive mixture independent component analysis.

NeuroImage Aug 4, 2018
There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formati...
Models, Theoretical Sleep Stages Humans Unsupervised Machine Learning Data Interpretation, Statistical Signal Processing, Computer-Assisted Adult Electroencephalography Wakefulness Cerebral Cortex
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