Automatic Sleep Stage Classification

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

14 shown of 14 papers with code (25 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Time-Series Representation Learning via Temporal and Contextual Contrasting
  26 Jun 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  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.
• Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
  31 Oct 2019 2 repositories listed Syntology ran 3 of 22 samples · 19 unverified
  We developed a framework to compare automated approaches to a consensus of multiple human scorers.
• MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification
  6 Jan 2025 1 repository listed
  Recent advancements in machine learning-based signal analysis, coupled with open data initiatives, have fuelled efforts in automatic sleep stage classification.
• Contrastive Learning for Sleep Staging based on Inter Subject Correlation
  5 May 2023 1 repository listed
  In recent years, multitudes of researches have applied deep learning to automatic sleep stage classification.
• Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
  15 Aug 2022 1 repository listed
  Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification.
• Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
  15 Jul 2022 1 repository listed
  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.
• ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
  9 Jul 2021 1 repository listed
  Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.
• An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
  28 Apr 2021 1 repository listed
  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.
• RobustSleepNet: Transfer learning for automated sleep staging at scale
  7 Jan 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics.
• Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
  21 Aug 2020 1 repository listed
  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…
• GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification
  9 Jul 2020 1 repository listed
  However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging.
• MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
  8 Apr 2020 1 repository listed
  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…
• Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
  30 Jul 2019 1 repository listed
  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…
• Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
  16 May 2018 1 repository listed
  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…

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.