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Objective: Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very
time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the
accuracy of sleep stage scoring and classification for sleep analysis.
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