Sleep Staging
37 papers with code • 0 benchmarks • 3 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
Sleep-EDF
Montreal Archive of Sleep Studies
Most implemented papers
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Methods:We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between Jan. 1st of 2010 and Feb. 29th of 2020 from Google Scholar, PubMed, and the DBLP.
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.
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
IoBT-VISTEC/MetaSleepLearner
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This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images.
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification
ziyujia/GraphSleepNet
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International Joint Conference on Artificial Intelligence 2020
However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging.
RobustSleepNet: Transfer learning for automated sleep staging at scale
Dreem-Organization/RobustSleepNet
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Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics.
SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging
Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage.
Sleep syndromes onset detection based on automatic sleep staging algorithm
In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules.
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.
Self-supervised Contrastive Learning for EEG-based Sleep Staging
In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning.
