A rule-based sleep staging method operationalizing AASM scoring rules achieves 60.5% agreement with human majority-vote consensus on 50 PSG recordings while providing epoch-level explanations.
Rosenberg and Steven Van Hout
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Mamba model reaches 84% balanced accuracy on 3-class sleep staging from multimodal wearable data without EEG in 357 adults with concurrent PSG.
citing papers explorer
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Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
A rule-based sleep staging method operationalizing AASM scoring rules achieves 60.5% agreement with human majority-vote consensus on 50 PSG recordings while providing epoch-level explanations.
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Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
Mamba model reaches 84% balanced accuracy on 3-class sleep staging from multimodal wearable data without EEG in 357 adults with concurrent PSG.