StageGuard: Physiologically Constrained Sleep Staging

2026-07-25

AI Summary

This study addresses a critical limitation of current automatic sleep staging models: despite high classification accuracy, they often produce hypnograms that violate physiological constraints—such as implausible state transitions or excessive fragmentation—leading to biased downstream metrics. To resolve this, the authors propose StageGuard, a plug-and-play, backbone-agnostic structured inference framework that jointly enforces state transition rules and minimum duration constraints during both training and decoding via a differentiable soft transition penalty and a semi-Markovian constrained decoder. By seamlessly integrating physiological priors as soft constraints into deep staging pipelines, StageGuard significantly enhances output plausibility while preserving rare pathological events. Experiments across six backbone architectures and four datasets demonstrate that StageGuard reduces transition violations to physiologically plausible levels, decreases fragmentation by 56–62%, maintains or slightly improves classification accuracy, and cuts errors in derived clinical metrics by 59–79%, thereby more accurately capturing subgroup effects.

Abstract

Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics. Deep learning models achieve epoch-level accuracy approaching inter-rater agreement, yet often produce hypnograms that violate physiological invariants, such as rare transitions (e.g., direct Wake -> REM) or excessively fragmented sequences. Such violations can bias downstream sleep metrics, regardless of overall accuracy. We propose StageGuard, a plug-and-play, backbone-agnostic structured-inference framework that wraps any neural sleep-staging backbone with physiology-informed priors. StageGuard combines (1) a differentiable soft transition penalty that discourages physiologically rare transitions during training, and (2) a semi-Markov constrained decoder with a duration-augmented state space that jointly enforces transition penalties and minimum bout durations at inference. Unlike hard-prohibition methods, it admits rare transitions when emission evidence is overwhelming, leaving informative pathological events recoverable rather than blocked. StageGuard constrains staging outputs to satisfy known physiological priors rather than modeling sleep generatively. We quantify the validity gap using transition-violation rate (TVR) and fragmentation index (FI) and demonstrate that, across six backbones and four datasets, StageGuard reduces TVR to physiologically plausible levels and lowers FI by 56-62%, while maintaining or slightly improving classification accuracy. Crucially, improved constraint satisfaction translates into 59-79% lower error on derived sleep-architecture statistics not directly optimized by the method, and recovers the direction and effect size of expert-defined subgroup differences (OSA severity, age) more faithfully than the unconstrained baseline.

Problem

• sleep staging
• physiological constraints
• hypnogram validity
• transition violations
• sleep fragmentation

Innovation

• StageGuard
• physiological constraints
• sleep staging
• structured inference
• semi-Markov decoder