发表机构
Zu Chongzhi Center, Duke Kunshan University（昆山杜克大学祖冲之中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对深度学习睡眠分期模型常产生违反生理不变性结果的问题，提出StageGuard框架，结合可微软过渡惩罚和半马尔可夫约束解码器，在多数据集和主干上降低违规率与碎片化指数，保持或提升准确率，降低派生统计数据误差。
AI 中文摘要
自动化睡眠分期在大规模研究中越来越多地用于得出睡眠结构端点。深度学习模型虽能达到接近评分者间一致性的epoch级准确率，但常产生违反生理不变性的睡眠图。我们提出StageGuard，一个即插即用、与主干无关的结构化推理框架，用生理学先验知识包裹任何神经睡眠分期主干。它结合了可微软过渡惩罚和半马尔可夫约束解码器。通过过渡违规率和碎片化指数量化有效性差距，实验表明StageGuard在六个主干和四个数据集上降低了过渡违规率，将碎片化指数降低了56 - 62%，同时保持或略微提高了分类准确率，还降低了派生睡眠结构统计数据的误差。
英文摘要
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.
Comments12 pages. Accepted at KDD 2026 (32nd ACM SIGKDD Conference), AI for Sciences track
