Sleep Staging
36 papers with code • 0 benchmarks • 1 datasets
Human Sleep Staging into W-R-N or W-R-L-D classes from multiple or single polysomnography signals
Benchmarks
These leaderboards are used to track progress in Sleep Staging
Libraries
Use these libraries to find Sleep Staging models and implementations
Montreal Archive of Sleep Studies
Most implemented papers
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
perslev/U-Time
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NeurIPS 2019
We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data.
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.
Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG
kylemath/DeepEEG
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We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG.
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Dreem-Organization/dreem-learning-open
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We developed a framework to compare automated approaches to a consensus of multiple human scorers.
Uncovering the structure of clinical EEG signals with self-supervised learning
zacharycbrown/ssl_baselines_for_biosignal_feature_extraction
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Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
pquochuy/MultitaskSleepNet
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While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways.
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets
mHealthBuet/ASSC
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The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6. 8% and 6. 3% on the household data and multi-source data, respectively.
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
pquochuy/sleep_transfer_learning
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We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database.
Screening for REM Sleep Behaviour Disorder with Minimal Sensors
This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.
