[HTML][HTML] Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive
task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However …
task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However …
A residual based attention model for EEG based sleep staging
Sleep staging is to score the sleep state of a subject into different sleep stages such as
Wake and Rapid Eye Movement (REM). It plays an indispensable role in the diagnosis and …
Wake and Rapid Eye Movement (REM). It plays an indispensable role in the diagnosis and …
[HTML][HTML] An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
End-to-end sleep staging using convolutional neural network in raw single-channel EEG
Objective Manual sleep staging on overnight polysomnography (PSG) is time-consuming
and laborious. This study aims to develop an end-to-end automatic sleep staging method in …
and laborious. This study aims to develop an end-to-end automatic sleep staging method in …
A deep learning approach for real-time detection of sleep spindles
PM Kulkarni, Z Xiao, EJ Robinson… - Journal of neural …, 2019 - iopscience.iop.org
Objective. Sleep spindles have been implicated in memory consolidation and synaptic
plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection …
plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection …
CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets
Sleep relaxes and rests the body by slowing down the metabolism, making us physically
stronger and fitter when we wake up. However, in a sleep disorder that may occur in …
stronger and fitter when we wake up. However, in a sleep disorder that may occur in …
Automatic sleep staging employing convolutional neural networks and cortical connectivity images
P Chriskos, CA Frantzidis, PT Gkivogkli… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …
and physical well-being and pathological conditions. A prerequisite for further analysis is the …
U-time: A fully convolutional network for time series segmentation applied to sleep staging
Neural networks are becoming more and more popular for the analysis of physiological time-
series. The most successful deep learning systems in this domain combine convolutional …
series. The most successful deep learning systems in this domain combine convolutional …
L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal
dependency in the sleep data. Yet, exploring this long-term dependency when developing …
dependency in the sleep data. Yet, exploring this long-term dependency when developing …
SHNN: A single-channel EEG sleep staging model based on semi-supervised learning
Sleep staging is an essential step in the diagnosis and treatment of sleep-related diseases.
Currently, most supervised learning models face the problem of insufficient labeled data. In …
Currently, most supervised learning models face the problem of insufficient labeled data. In …