AIMC Topic: Sleep Stages
Clear Filters Showing 141 to 150 of 222 articles
Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images.
IEEE transactions on neural networks and learning systems
Mar 15, 2019
Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prerequisite for further analysis is the identification of the sleep macroarchitecture through manual sle...
Sleep staging from single-channel EEG with multi-scale feature and contextual information.
Sleep & breathing = Schlaf & Atmung
Mar 12, 2019
PURPOSE: Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which e...
A deep learning approach for real-time detection of sleep spindles.
Journal of neural engineering
Feb 21, 2019
OBJECTIVE: Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.
A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.
International journal of environmental research and public health
Feb 19, 2019
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environme...
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 31, 2019
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification...
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals.
Computers in biology and medicine
Jan 19, 2019
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study prot...
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).
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...
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,...
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...
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