Multimodal Sleep Stage Detection
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Benchmarks
Most implemented
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Papers
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinic…
Contrastive LearningDiagnosticEEGElectroencephalogram (EEG)+6Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and in particular, deep-learning based algori…
Automatic Sleep Stage ClassificationDeep LearningGeneral ClassificationMultimodal Sleep Stage Detection+3Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, ofte…
Automatic Sleep Stage ClassificationBIG-bench Machine LearningDeep LearningMultimodal Sleep Stage Detection+2Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been design…
Automatic Sleep Stage ClassificationMultimodal Sleep Stage DetectionSleep Stage DetectionSleep StagingTowards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability…
Automatic Sleep Stage ClassificationMultimodal Sleep Stage DetectionSleep Stage DetectionSleep Staging+1Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnos…
Multimodal Sleep Stage DetectionSleep Staging