Multimodal Sleep Stage Detection
4 papers with code • 4 benchmarks • 1 datasets
Using multiple modalities such as EEG+EOG, EEG+HR instead of just relying on EEG (polysomnography)
Benchmarks
These leaderboards are used to track progress in Multimodal Sleep Stage Detection
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Sleep-EDF
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.
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.
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.
