Sleep Stage Detection
19 papers with code • 15 benchmarks • 6 datasets
Human Sleep Staging into W-N1-N2-N3-REM classes from multiple or single polysomnography signals
Benchmarks
These leaderboards are used to track progress in Sleep Stage Detection
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Sleep-EDF
MaSS
Montreal Archive of Sleep Studies
ISRUC-Sleep
Most implemented papers
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave
The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.
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.
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.
Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG
gist-ailab/IITNet-official
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A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring.
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.
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
duggalrahul/REST
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By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.
