AI RESEARCH
Sleep-stage efficient classification using a lightweight self-supervised model
arXiv CS.CV
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ArXi:2605.26295v1 Announce Type: new Accurate classification of sleep stages is crucial for diagnosing sleep disorders and automating this process can significantly enhance clinical assessments. This study aims to explore the use of a self-supervised model ( specifically, an adapted version of mulEEG) combined with a Linear SVM classifier to improve sleep stage classification.