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Introduces Random Attention (RA), a lightweight temporal modeling module for mobile sleep staging that uses fixed random projections for similarity-based aggregation, achieving competitive performance with minimal additional parameters.
This paper presents a deterministic, rule-based sleep staging method that explicitly implements the American Academy of Sleep Medicine (AASM) scoring rules, providing epoch-level natural language explanations. It achieves 60.5% epoch-level agreement with a majority-vote consensus on 50 polysomnography recordings, offering transparency as a complement to opaque deep learning models.
ConfSleepNet is a conflict-aware evidential framework for reliable sleep stage classification using multi-modal data. It introduces hybrid category structures and a conflict-aware aggregation method to resolve inter-view conflicts, demonstrating effectiveness on sleep staging tasks.