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Multimodal Sleep Stage Detection

Using multiple modalities such as EEG+EOG, EEG+HR instead of just relying on EEG (polysomnography)

Papers

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Showing 1–6 of 6 papers

TitleStatusHype
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning FrameworkCode1
Toward Interpretable Sleep Stage Classification Using Cross-Modal TransformersCode1
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep ScoringCode1
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep stagingCode0
Towards More Accurate Automatic Sleep Staging via Deep Transfer LearningCode0
Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram—0
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