AIMC Topic: Sleep Stages

Clear Filters Showing 151 to 160 of 222 articles

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...
Humans Machine Learning Electroencephalography Actigraphy Sleep Wake Disorders Sleep Stages Female Adult Male
View on PubMed DOI

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...
Humans Young Adult Adult Male Middle Aged Neural Networks, Computer Female Aged Adolescent Sleep Stages Signal Processing, Computer-Assisted Electrodiagnosis Polysomnography
View on PubMed DOI

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...
Actigraphy Sleep Stages Signal Processing, Computer-Assisted Wrist Heart Rate Neural Networks, Computer Female Young Adult Middle Aged Wearable Electronic Devices Adult Humans Male
View on PubMed DOI

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 Biological Ontologies Sleep Stages Male Electroencephalography Humans Sleep Data Analysis Artifacts Female
View on PubMed DOI

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...
Automation Infant, Premature Sleep Stages Electroencephalography Wakefulness Sleep Female Infant, Newborn Male Algorithms Brain Humans Neural Networks, Computer
View on PubMed DOI

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...
Wakefulness Cerebral Cortex Sleep Stages Data Interpretation, Statistical Electroencephalography Signal Processing, Computer-Assisted Models, Theoretical Unsupervised Machine Learning Humans Adult
View on PubMed DOI

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 Humans Neural Networks, Computer Unsupervised Machine Learning Male Electroencephalography Middle Aged Sleep Stages Deep Learning Female Wearable Electronic Devices Aged Adult
View on PubMed DOI

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...
Electroencephalography Polysomnography Sleep Stages Humans Machine Learning Time Factors
View on PubMed DOI

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...
Models, Theoretical Electroencephalography Signal Processing, Computer-Assisted Sleep Stages Humans Neural Networks, Computer Machine Learning Algorithms
View on PubMed DOI

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...
Wrist Heart Rate Thorax Sleep Stages Actigraphy Polysomnography Male Humans Middle Aged Female Support Vector Machine Adult
View on PubMed DOI
Popular Topics
Recent Journals