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
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.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
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.
A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
acrophase/sleep_staging_kd
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This demonstrates the viability of KD for performance improvement of single-channel ECG based sleep staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models.
Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers
jathurshan0330/cross-modal-transformer
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Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification.
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
Conventionally, learning-based automatic sleep scoring on single-channel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult.
Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
Raminghorbanii/Self-Supervised-PPG-Representation-Learning-Shows-High-Inter-Subject-Variability
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Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce.
Structure-Preserving Transformers for Sequences of SPD Matrices
mathieuseraphim/spdtransnet
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In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean geometries.
