Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification
Sleep stage classification is essential for sleep assessment and disease diagnosis.
Although previous attempts to classify sleep stages have achieved high classification …
Although previous attempts to classify sleep stages have achieved high classification …
[PDF][PDF] GraphSleepNet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification.
Sleep stage classification is essential for sleep assessment and disease diagnosis.
However, how to effectively utilize brain spatial features and transition information among …
However, how to effectively utilize brain spatial features and transition information among …
An attention-based deep learning approach for sleep stage classification with single-channel EEG
Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …
quality. In this paper, we propose a novel attention-based deep learning architecture called …
A two-stage neural network for sleep stage classification based on feature learning, sequence learning, and data augmentation
Sleep stage classification is a fundamental but cumbersome task in sleep analysis. To score
the sleep stage automatically, this study presents a stage classification method based on a …
the sleep stage automatically, this study presents a stage classification method based on a …
SleepFCN: A fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms
Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In
order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a …
order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a …
A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model
B Yang, X Zhu, Y Liu, H Liu - Biomedical Signal Processing and Control, 2021 - Elsevier
Sleep stage classification is an essential process for analyzing sleep and diagnosing sleep
related disorders. Sleep staging by visual inspection of expert is a labor-intensive task and …
related disorders. Sleep staging by visual inspection of expert is a labor-intensive task and …
A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from EEG signals
Sleep is an essential integrant in everyone's daily life. Thereby, it is an important but
challenging problem to construct a reliable and stable system, that can monitor user's sleep …
challenging problem to construct a reliable and stable system, that can monitor user's sleep …
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
S Chambon, MN Galtier, PJ Arnal… - … on Neural Systems …, 2018 - ieeexplore.ieee.org
Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
A hybrid self-attention deep learning framework for multivariate sleep stage classification
Background Sleep is a complex and dynamic biological process characterized by different
sleep patterns. Comprehensive sleep monitoring and analysis using multivariate …
sleep patterns. Comprehensive sleep monitoring and analysis using multivariate …
Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG
J Zhang, R Yao, W Ge, J Gao - Computer methods and programs in …, 2020 - Elsevier
Background and objective In recent years, several automatic sleep stage classification
methods based on convolutional neural networks (CNN) by learning hierarchical feature …
methods based on convolutional neural networks (CNN) by learning hierarchical feature …