Automatic Sleep Stage Classification
13 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Automatic Sleep Stage Classification
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Sleep-EDF
Montreal Archive of Sleep Studies
ISRUC-Sleep
Latest papers with no code
Evaluating sleep-stage classification: how age and early-late sleep affects classification performance
Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability.
SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages
Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities.
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals
This sleep stage classification model could be adapted to chronic and continuous monitor sleep for Parkinson's patients in daily life, and potentially utilized for more precise treatment in deep brain-machine interfaces, such as closed-loop deep brain stimulation.
A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data.
A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick
In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture.
End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
For five sleep stage classification, the classification performance 85. 6% and 91. 1% using the raw input data and the proposed input, respectively.
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models.
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals.
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification
Automatic sleep staging is a challenging problem and state-of-the-art algorithms have not yet reached satisfactory performance to be used instead of manual scoring by a sleep technician.
Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device
The feature learning framework is designed to extract low- and mid-level features.
