Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
Automated sleep staging is commonly approached as a supervised machine learning problem, with deep learning methods dominating recent research. While machine learning models achieve near-human level agreement with human-scored reference sleep stages, their decisions are typically opaque and not designed to follow clinical scoring rules. We propose a transparent alternative: a deterministic, rule-based sleep staging method that explicitly operationalizes the American Academy of Sleep Medicine's (AASM) scoring logic as executable code, coupled with epoch-level natural-language justifications derived from an explanation trace. We evaluate the approach on 50 polysomnography recordings with a ...
| Authors | Emil Hardarson, Konstantin Popov, Sigridur Sigurdardottir et al. |
|---|---|
| Primary category | eess.SP |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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