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
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Sleep-EDF
Montreal Archive of Sleep Studies
ISRUC-Sleep
Most implemented papers
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.
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.
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.
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.
Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
neergaard/deep-sleep-pytorch
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We applied four different scenarios: 1) impact of varying time-scales in the model; 2) performance of a single cohort on other cohorts of smaller, greater or equal size relative to the performance of other cohorts on a single cohort; 3) varying the fraction of mixed-cohort training data compared to using single-origin data; and 4) comparing models trained on combinations of data from 2, 3, and 4 cohorts.
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.
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.
Time-Series Representation Learning via Temporal and Contextual Contrasting
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
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.
